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# Chapter 3. Asymptotic Methods

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### 1 Modes of Convergence of A Sequence of Random Vari- ables

Due to the difficulty of making exact calculation, we make use of asymptotic results. For example, we experience the approximation of probabilities for computing significance levels and setting confidence. In this process, we use the following facts: Law of Large Numbers, Central Limit Theorem, and the Approximation of Binomial Distribution by Normal Distribution or Poisson Distribution and etc.. The essence of asymptotic methods is approximation.

We approximate functions, random variables, probability distributions, means, variances, and covariance. However, we need to understand what kind of ap- proximation we are using. The strong law of large numbers and the central limit theorem illustrate the two main types of limit theorems in probability.

Strong limit theorems. Given a sequence of functions X1(w), X2(w), . . . there is a limit function X(w) such that P (w : limnXn(w) = X(w)) = 1.

Weak limit theorems. Given a sequence of functions X1(w), X2(w), . . . show that limnP (w : Xn(w) < x) exists for every x.

There is a great difference between strong and weak theorems which will become more apparent. A more dramatic example of this is: on ([0, 1), B1([0, 1))) with P being Lebesgue measure, define

Xn(w) =

0, w < 12, 1, 12 ≤ w < 1, for n even. For n odd,

Xn(w) =

1, w < 12, 0, 12 ≤ w < 1.

For all n, P (w : Xn(w) < x) = P (w : X1(w) < x). But for every w ∈ [0, 1) lim sup

n

Xn(w) = 1, lim inf

n Xn(w) = 0.

In this chapter, we will attempt to understand these asymptotic calculation.

1.1 The O, o Notation

Before the discussion of the concept of convergence for random variable, we will give a quick review of ways of comparing the magnitude of two sequences. A

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notation that is especially useful for keeping track of the order of an approx- imation is the “big O, little o.” Let {an} and {βn} be two sequences of real numbers. We have the following three concept of comparison:

an= O(βn) if the ratio ann is bounded for large n, if there exists a number K and an integer n(K) such that if n ≥ K, then |an| < K|bn|.

an= o(βn) if the ratio ann converges to 0, as n → ∞.

an∼ βn iff ann = c + o(1), c 6= 0, as n → ∞.

Fact. (1) O(an)O(βn) = O(anβn), (2) O(an)o(βn) = o(anβn), (3) o(an)o(βn) = o(anβn),

(4) o(1) + O(n−1/2) + O(n−1) = o(1). The order of magnitude of a finite sum is the largest order of magnitude of the summands.

Example. Taylor expansion of a function f (·) about the value c can be stated as

f (x) = f (c) + (x − c)f0(c) + o(|x − c|) as x → c.

In general,

Theorem 1 (Taylor). Let the function f have a finite nth derivatives f(n) everywhere in the open interval (a, b) and (n − 1)th derivative f(n−1)continuous in the closed interval [a, b]. Let x ∈ [a, b]. For each point y ∈ [a, b], y 6= x, there exists a point z interior to the interval joining x and y such that

f (y) = f (x) +

n−1

X

k=1

f(k)(x)

k! (y − x)k+f(n)(z)

n! (y − x)n. or

f (y) = f (x) +

n−1

X

k=1

f(k)(x)

k! (y − x)k+ o(|y − x|n−1) as y → x.

1.2 Convergence of Stochastic Sequences

Now we consider probabilistic version of these order of magnitude relations. Let An and Bn be sequences of real random variables. Then

An= Op(Bn) iff for every  > 0, there exists a constant M () and an integer N () such that if n ≥ N (), then

P {|An/Bn| ≤ M ()} ≥ 1 − .

An= op(Bn) iff for every  > 0, limn→∞P {|An/Bn| ≤ } = 1.

An≈ Bn iff An = Bn+ op(Bn).

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If Xn is a vector, we say that Xn = opn) if kXnk = opn). Here kXnk denotes the length of the vector Xn.

Let X1, X2, . . . and X be random variables on a probability space (Ω, A, P ).

As an example, we take a measurement from an experiment in a laboratory.

Usually the outcome of the experiment cannot be predicted with certainty. To handle this situation, we introduce the probability of A (a collection of possible outcomes) to be the fraction of times that the outcome of the experiment results in A in a large number of trials of the experiment. The set of all outcomes of an experiment are called elementary events. Here, Ω is the set of all elementary events which is also called the sample space. A is a class of subsets of Ω to which we can assign probability. For each set A ∈ A we assign a value P (A) to be called the probability of A. Note that P is a set function over the members of A.

What kind of A would suffice our need? From our experience, four kinds of operations on sets, which are intersection, complement, union and set difference, are convenient and useful tools describing events. It is then quite natural to require that A contains the event formed by such operations. Such a class of sets is called a Boolean field. Based on the need, we also like to consider unions of all countable sequences of sets (events). We therefore require that A to be a Borel field or a σ-field. It means that it contains unions of all countable sequences of sets (and therefore countable intersections) and complementation.

For example, Ω can be a set of numbers or a subinterval of the real line.

The context that is necessary for the strong limit theorems we want to prove is:

Definition A probability space consists of a triple (Ω, F , P ) where

(i) Ω is a space of points w, called the sample space and sample points. It is a nonempty set that represents the collection of all possible outcomes of an experiment.

(ii) F is a σ-field of subsets of Ω. It includes the empty set as well as the set Ω and is closed under the set operations of complements and finite or countable unions and intersections. The elements of F are called measurable events, or simply events.

(iii) P (·) is a probability measure on F ; henceforth refer to P as simply a probability. It is an assignment of probabilities to events in F that is subject to the conditions that

1. 0 ≤ P (F ) ≤ 1, for each F ∈ F ,

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2. P (Ø) = 0, P (Ω) = 1,

3. P (∪iFi) = PiP (Fi) for any finite or countable sequence of mutually exclusive events Fi, i = 1, 2, . . ., belonging to F .

### 2 Remarks on measure and integration

A pair (Ω, F ) consisting of a set Ω and a σ-field F of subsets of Ω is called a measurable space. For any given Ω, there is one trivial σ-field which is the collection containing exactly two elements, empty set and Ω. However, this field cannot be useful in applications.

Consider the set of real numbers R, which is uncountably infinite. We define the Lebesgue measure of intervals in R to be their length. This definition and the properties of measure determine the Lebesgue measure of many, but not all, subsets of R. The collection of subsets of R we consider, and for which Lebesgue measure is defined, is the collection of Borel sets defined below.

Let C be the collection of all finite open intervals on R. Then B = σ(C) is called the Borel σ-field. The elements of B are called Borel sets.

• All intervals (finite or infinite), open sets, and closed sets are Borel sets.

These can be shown easily by the following.

(a, ∞) = ∪n=1(a, a + n), (−∞, a) = ∪n=1(a − n, a), [a, b] = ((−∞, a) ∪ (b, ∞))c, [a, ∞) = ∪n=1[a, a + n), (−∞, a] = ∪n=1[a − n, a), (a, b] = (−∞, b] ∩ (a, ∞), {a} = ∩



a − 1

n, a + 1 n



.

This means that every set containing countably infinitely many numbers is Borel; if A = {a1, a2, . . .}, then

A = ∪k=1{ak}.

Hence the set of rational numbers is Borel, as is its complement, the set of irrational numbers. There are, however, sets which are not Borel. We have just seen that any non-Borel set must have uncountably many points.

• B = σ(O), where O is the collection of all open sets.

• The Borel σ-field Bk on the k-dimensional Enclidean space Rk can be similarly defined.

• Let C ⊂ Rkbe a Borel set and let BC = {C ∩B : B ∈ Bk}. Then (C, BC) is a measurable space and BC is called the Borel σ-field on C. (In statistics, it is quite often that we need to consider conditional probability and etc.)

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The closure properties of F ensure that the usual applications of set op- erations in representing events do not lead to nonmeasurable events for which no (consistent) assignment of probability is possible.

The required countable additivity property (3) gives probabilities a suffi- ciently rich structure for doing calculations and approximations involving lim- its. Two immediate consequences of (3) are the following so-called continuity properties: if A1 ⊂ A2 ⊂ · · · is a nondecreasing sequence of events in F then, thinking of ∪n=1An as the limiting event for such sequences,

P (∪n=1An) = lim

n P (An).

To prove this, disjointify {An} by Bn = An− An−1, n ≥ 1, A0 = Ø, and apply (iii) to ∪n=1Bn= ∪n=1An. By considering complements, one gets for decreasing measurable events A1 ⊃ A2 ⊃ · · · that

P (∪n=1An) = lim

n P (An).

Example 1. Suppose that {Xt : 0 ≤ t < ∞} is a continuous-time Markov chains with a finite or countable state space S. The Markov property here refers to the property that the conditional distribution of the future, given past and present states of the process, does not depend on the past. The conditional probabilities pij(s, t) = P (Xt = j|Xs = i), 0 ≤ s < t, are collectively referred to as the transition probability law for the process. In the case pij(s, t) is a function of t − s, the transition law is called time-homogeneous, and we write pij(s, t) = pij(t − s). Write p(t0) = ((pij(t0)), where pij(t0) gives the probability that the process will be in state j at time t0 if it is initially at state i. We assume that limt→0p(t) = I, where I is the identity matrix. It means that with probability 1, the process spends a positive (but variable) amount of time in the initial state i before moving to a different state j. Set

qij = lim

t→0

pij(t) − pij(0)

t = lim

t→0

pij(t) − δ t

which is being referred to as the infinitesimal transition rates. Write Q = ((qij)), the infinitesimal generator.

Assume the Markov chain have the initial state i and let T0 = inf{t > 0 : Xt6= i}. An important question is finding the distribution of T0.

Let A denote the event that {T0 > t}. Choose and fix t > 0. For each integer n ≥ 1 define the finite-dimensional event

An = {X(m/2n)t = i for m = 0, 1, . . . , 2n}.

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The events An are decreasing as n increases and A = lim

n→∞An= ∩n=1An

= {Xu = i for all u in [0, t] which is a binary rational multiple of t}

= {T0 > t}.

Since there is some u of the form u = (m/2n)t ≤ t in every nondegenerate interval, it follows that T0 has an exponential distribution with parameter −qii.

2.0.1 The Homogeneous Poisson Process, the Poisson Distribution and the Ex- ponential Distribution

In survival analysis, we are often interested in studying whether a particular event occurs or not. In this case, we can think in terms of death (denoted by state 1) and survive (denoted by state 0) using the language of Markov chain.

We just describe a very special chain with two states and state 1 is an absorbing state. As an illustration, we now consider a simplest chain in which there are only two states, 0 and 1. Usually, we would like to know the sojourn time of staying at state 0. Denote the sojourn time of staying at state 0 by T . We know that

P (T < t + δ|T ≥ t) ≈ λ(t)δ,

where λ(t) is the hazard function of T . Let T0 denote a fix time and δ = T0/n where n ∈ N . Using Markov property, we have

P (T ≥ T0) = P



T ≥ (n − 1)T0 δ



P



T ≥ T0

T ≥ (n − 1)T0 δ



= P



T ≥ (n − 2)T0 δ



P



T ≥ (n − 1)T0 δ

T ≥ (n − 2)T0 δ



·P



T ≥ T0

T ≥ (n − 1)T0 δ



. Continue in this fashion, we have

P (T ≥ T0) ≈ Y

i



1 − λ



iT0 δ



= exp

( X

i

ln



1 − λ



iT0 δ

)

≈ exp

( X

i



1 − λ



iT0

δ

)

→ exp

"

Z T0

0

λ(t)dt

#

.

This is the commonly seen form of survival function written in terms of the hazard function. If λ(t) = λ0, T is exponential distributed random variable.

Now we consider a different kind of chains with no absorbing states. This is usually seen in terms of Poisson Processes and Queues. The occurrences of

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a sequence of discrete events can often be realistically modelled as a Poisson process. The defining characteristic of such a process is that the time intervals between successive events are exponentially distributed. Now it is still a multi- state Markov chain with no absorbing state. For the purpose of illustration, we describe discrete-time Markov chains. In such a chain, it is often being discussed in terms of finite, aperiodic, and irreducible. Finiteness means that there is a finite number of possible states. The aperiodicity assumption is that there is no state such that a return to, that state is possible only at t0, 2t0, 3t0, . . . transitions later, where t0 is an integer exceeding 1. If the transition matrix of a Markov chain with states El, E2, E3, E4 is, for example,

P =

0 0 0.6 0.4 0 0 0.3 0.7 0.5 0.5 0 0 0.2 0.8 0 0

,

then the Markov chain is periodic. If the Markovian random variable starts (at time 0) in E1 then at time 1 it must be either in E3 or E4, at time 2 it must be in either El or E2, and in general it can visit only El at times 2, 4, 6, . . .. It is therefore periodic. The irreducibility assumption implies that any state can eventually be reached from any other state, if not in one step then after several steps except for the case of Markov chains with absorbing states.

Now we come back to the chain associated with Poisson process. Given a sequence of discrete events occurring at times t0, t1, t2, t3, . . . the intervals between successive events are 4t1 = (t1−t0), 4t2 = (t2−t1), 4t3 = (t3−t2), . . ., and so on. Assume the transition law is time-homogeneous. By the above argument, 4ti is again exponentially distributed. Due to the definition of Markov chain, these intervals 4ti are treated as independent random variables drawn from an exponentially distributed population, i.e., a population with the density function f (x) = λ exp(−λx) for some fixed constant λ.

Now we state the fundamental properties that define a Poisson process, and from these properties we derive the Poisson distribution. Suppose that a sequence of random events occur during some time interval. These events form a homogeneous Poisson process if the following two conditions are met:

(1) The occurrence of any event in the time interval (a, b) is independent of the occurrence of any event in the time interval (c, d), while (a, b) and (c, d) do not overlap.

(2) There is a constant λ > 0 such that for any sufficiently small time interval,

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(t, t + h), h > 0, the probability that one event occurs in (t, t + h), is independent of t, and is λh + o(h), and the probability that more than one event occurs in the interval (t, t + h) is o(h).

Condition 2 has two implications. The first is time homogeneity: The proba- bility of an event in the time interval (t, t + h) is independent of t. Second, this condition means that the probability of an event occurring in a small time interval is (up to a small order term) proportional to the length of the interval (with fixed proportionality constant λ). Thus the probability of no events in the interval (t, t + h) is 1 − λh + o(h), and the probability of one or more events in the interval (t, t + h) is λh + o(h).

Various naturally occurring phenomena follow, or very nearly follow, these two conditions. Suppose a cellular protein degrades spontaneously, and the quantity of this protein in the cell is maintained at a constant level by the continual generation of new proteins at approximately the degradation rate.

The number of proteins that degrade in any given time interval approximately satisfies conditions 1 and 2. The justification that condition 1 can be assumed in the model is that the number of proteins in the cell is essentially constant and that the spontaneous nature of the degradation process makes the independence assumption reasonable. Through time division and using Bernoulli random variable to indicate whether such an event occurs in (t, t + h), Condition 2 also follows when np is small, the probability of at least one success in n Bernoulli trials is approximately np.

We now show that under conditions 1 and 2, the number N of events that occur up to any arbitrary time t has a Poisson distribution with parameter λt.

At time 0 the value of N is necessarily 0, and at any later time t, the possible values of N are 0, 1, 2, 3, . . .. We denote the probability that N = j at any given time t by Pj(t). We would like to assess how Pj(t) behaves as a function of j and t.

The event that N = 0 at time t + h occurs only if no events occur in (0, t) and also no events occur in (t, t + h). Thus for small h,

P0(t + h) = P0(t)(1 − λh + o(h)) = P0(t)(l − λh) + o(h).

This equality follows from conditions 1 and 2.

The event that N = 1 at time t + h can occur - in two ways. The first is that N = 1 at time t and that no event occurs in the time interval (t, t + h), the second is that N = 0 at time t and that exactly one event occurs in the

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time interval (t, t + h). This gives

P1(t + h) = P0(t)(λh) + P1(t)(1 − λh) + o(h),

where the term o(h) is the sum of two terms, both of which are o(h). Finally, for j = 2, 3, . . . the event that N = j at time t + h can occur in three different ways. The first is that N = j at time t and that no event occurs in the time interval (t, t + h). The second is that N = j − 1 at time t and that exactly one event occurs in (t, t + h). The final possibility is that N < j − 2 at time t and that two or more events occur in (t, t + h). Thus, for j = 2, 3, . . .,

Pj(t + h) = Pj−l(t)(λh) + Pj(t)(l − λh) + o(h).

The above discussion leads to P0(t + h) − P0(t)

h = −P0(t)(λh) + o(h) h

Pj(t + h) − Pj(t)

h = −Pj−1(t)(λh) − Pj(t(λh)) + o(h)

h ,

j = 1, 2, 3, . . .. Letting h → 0, we get, d

dtP0(t) = −λP0(t),

and d

dtPj(t) = λPj−l(t) − λPj(t), j = 1, 2, 3, . . . . The Pj(t) are subject to the conditions

P0(0) = 1, Pj(0) = 0, j = 1, 2, 3, . . . .

The probability of the system still being in state 0 at time t, P0(t) = exp(−λt), which can be obtained easily. Note that P0(t) + P1(t) = 1. We could replace P0 with 1 − P1 and write this as

1 λ

dP1(t)

dt + P1(t) = 1.

From this

d

dt(P1(t) exp(λt)) = λ.

We have

P1(t) = e−λtλt.

By induction, the probability of the nth state at time t is Pn(t) = e−λt(λt)n

n! .

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This is the probability distribution for a simple Poisson counting process, rep- resenting the probability that exactly n events will have occurred by the time t. Obviously the sum of these probabilities for n = 1 to ∞ equals 1, because the exponential exp(−λt) factors out of the sum, and the sum of the remaining factors is just the power series expansion of exp(−λt).

It’s worth noting that since the distribution of intervals between successive occurrences is exponential, the Poisson distribution is stationary, meaning that any time can be taken as the initial time t = 0, which implies that the proba- bility of n occurrences in an interval of time depends only on the length of the interval, not on when the interval occurs. The expected number of occurrences by the time t is given by the integral

E(n, t) =

X

i=0

iPi(t) = λt.

Since the distribution of the time between successive events is given by the exponential distribution. Thus the (random) time until the kth event occurs is the sum of k independent exponentially distributed times. Let t0 be some fixed value of t. Then if the time until the kth evenn occurs exceeds t0, the number of events occurring before time t0 is less than k, and conversely. This means that the probability that k − 1 or fewer-events occur before time t0 must be identical to the probability that the time until the kth event occurs exceeds t0. In other words it must be true that

e−λt0 1 + (λt0) + (λt0)2

2! + · · · + (λt0)k−1 (k − 1)!

!

= λk Γ(k)

Z t0

xk−1exp(−λx)dx.

This equation can also be established by repeated integration by parts of the right-hand side.

2.1 Counting measure and Lebesgue measure

First, we consider the counting measure in which Ω is a finite or countable set.

Then probabilities are defined for all subsets F of Ω once they are specified for singletons, so F is the collection of all subsets of Ω. Thus, if f is a prob- ability mass function (p.m.f.) for singletons, i.e., f (w) ≥ 0 for all w ∈ Ω and

P

wf (w) = 1, then one may define P (F ) = Pw∈Ff (w). The function P so defined on the class of all subsets of Ω is countably additive, i.e., P satisfies (3).

So (Ω, F , P ) is easily seen to be a probability space. In this case the probability measure P is determined by the probabilities of singletons {w}.

In the case Ω is not finite or countable, e.g., when Ω is the real line or the space of all infinite sequences of 0’s and 1’s, then the counting measure

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formulation is no longer possible in general. We consider Lebesgue measure.

The Lebesgue measure µ0 of a set containing only one point must be zero. In fact, since

{a} ⊂



a − 1

n, a + 1 n



for every positive integer n, we must have µ0({a}) = 0. Hence, the Lebesgue measure of a set containing countably many points must also be zero. Instead, for example in the case Ω = R1, one is often given a piecewise continuous probability density function (p.d.f.) f , i.e., f is nonnegative, integrable, and

R

−∞f (x)dx = 1. For an interval I = (a, b) or (b, ∞), −∞ ≤ a < b ≤ ∞, one then assigns the probability P (I) = Rabf (x)dx, by a Riemann integral. The Lebesgue measure of a set containing uncountably many points can be either zero, positive and finite, or infinite. We may not compute the Lebesgue measure of an uncountable set by adding up the Lebesgue measure of its individual members, because there is no way to add up uncountably many numbers.

This set function P may be extended to the class C comprising all finite unions F = ∪jIj of pairwise disjoint intervals Ij by setting P (F ) = PjP (Ij).

The class C is a field, i.e., the empty set and Ω belong to C and it is closed under complements and finite intersection (and therefore finite unions). But, since C is not a σ field, usual sequentially applied operations on events may lead to events outside of C for which probabilities have not been defined. But a theorem from measure theory, the Caratheodory Extension Theorem, asserts that there is a unique countably additive extension of P from a field C to the smallest σ field that contains C. In the case of C above, this σ field is called the Borel σ field B1 on R1 and its sets are called Borel sets of R1.

In general, such an extension of P to the power set σ-field, that is the collection of all subsets of R1, is not possible. The same considerations apply to all measures (i.e., countably additive nonnegative set functions µ defined on a σ-field with µ(O) = 0), whether the measure of Ω is 1 or not. The measure µ0 = m, which is defined first for each interval I and the length of the interval, and then extend uniquely to B1, is called Lebesgue measure on R1. Similarly, one defines the Lebesgue measure on Rk (k ≥ 2) whose Borel σ-field Bk is the smallest σ field that contains all k-dimensional rectangles I = I1 × I2 × · · · × Ik, with Ij a one-dimensional rectangle (interval) of the previous type. The Lebesgue measure of a rectangle is the product of the lengths of its sides, i.e., its volume. Lebesgue measure on Rk has the property that the space can be decomposed into a countable union measurable sets of finite Lebesgue measure; such measures are said to be sigma-finite. All measures

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referred to in this note are sigma-finite.

2.2 Extension

A finitely additive measure µ on a field F is a real-valued (including ∞), non- negative function with domain F such that for A, B ∈ F , A ∩ B = Ø,

µ(A ∪ B) = µ(A) + µ(B).

The extension problem for measures is: Given a finitely additive measure µ0

on a field F0, when does there exist a measure µ on F (F0) agreeing with µ0 on F0? A measure has certain continuity properties:

Theorem 2 Let µ be a measure on the σ-field F. If An ↓ A, An ∈ F , and if µ(An) < ∞ for some n, then

limn µ(An) = µ(A).

Also, if An↑ A, An∈ F , then

limn µ(An) = µ(A).

This is called continuity from above and below. Certainly, if µ0 is to be ex- tended, then the minimum requirement needed is that µ0 be continuous on its domain. Call µ0 continuous from above at Ø if whenever An∈ F0, An↓ Ø, and µ0(An) < ∞ for some n, then

limn µ0(An) = 0.

Consider the example that

A1 = [1, ∞), A2 = [2, ∞), A3 = [3, ∞), . . . . Then ∩k=1Ak = Ø, so µ(∩k=1Ak) = 0, but limn→∞µ(An) = ∞.

Caratheodory Extension Theorem. If µ0 on F0 is continuous from above at Ø, then there is a unique measure µ on F (F0) agreeing with µ0 on F0 (see Halmos, p. 54).

The extension of a measure µ from a field C, as provided by the Caratheodory Extension Theorem stated above, is unique and may be expressed by the for- mula

µ(F ) = infX

n

µ(Cn), (F ∈ F ),

where the summation is over a finite collection C1, C2, . . . of sets in C whose union contains F and the infimum is over all such collections.

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As suggested by the construction of measures on Bkoutlined above, start- ing from their specifications on a class of rectangles, if two measures µ1 and µ2 on a sigmafield F agree on subclass A ⊂ F closed under finite intersections and Ω ∈ A, then they agree on the smallest sigmafield, denoted σ(A), that contains A. The sigmafield σ(A) is called the σ-field generated by A. On a metric space S the σ-field B = B(S) generated by the class of all open sets is called the Borel σ field.

2.3 Lebesgue integral

An indicator function g from R to R is a function which takes only the values 0 and 1. We call

A = {x ∈ R; g(x) = 1}

the set indicated by g. We define the Lebesgue integral of g to be

Z

R

gdµ = µ0(A).

A simple function h from R to R is a linear combination of indicators, i.e., a function of the form h(x) =Pnk=1ckgk(x), where each gk is of the form

gk(x) =

1 if x ∈ Ak 0 if x 6∈ Ak

and each ck is a real number. We define the Lebesgue integral of h to be

Pn

k=1ckµ(Ak). Let f be a nonnegative function defined on R, possibly taking the value ∞ at some points. We define the Lebesgue integral of f to be

Z

R

f dµ0 = sup

Z

R

hdµ0; h is simple and h(x) ≤ f (x) for every x ∈ R



. It is possible that this integral is infinite. If it is finite, we say that f is integrable.

Finally, let f be a function defined on R, possibly taking the value ∞ at some points and the value −∞ at other points. We define the positive and negative parts of f to be

f+(x) = max{f (x), 0}, f(x) = max{−f (x), 0}, respectively, and we define the Lebesgue integral of f to be

Z

R

f dµ0 =

Z

R

f+0

Z

R

f0,

provided the right-hand side is not of the form ∞ − ∞. If both RRf+0 and

R

Rf) are finite (or equivalently, RR|f |dµ0 < ∞, since |f | = f++ f), we say that f is integrable.

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Let f be a function defined on R, possibly taking the value ∞ at some points and the value −∞ at other points. Let A be a subset of R. We define

R

Af dµ0 =RR1Af dµ0.

The Lebesgue integral just defined is related to the Riemann integral in one very important way: if the Riemann integral Rabf (x)dx is defined, then the Lebesgue integral R[a,b]f dµ0 agrees with the Riemann integral. The Lebesgue integral has two important advantages over the Riemann integral. The first is that the Lebesgue integral is defined for more functions, as we show in the following examples.

Example 2. Let Q be the set of rational numbers in [0, 1] and consider f = 1Q. Being a countable set, Q has Lebesgue measure zero, and so the Lebesgue integral of f over [0, 1] is R[0,1]f dµ0 = 0. To compute the Riemann integral

R1

0 f (x)dx, we choose partition points 0 = x0 < x1 < · · · < xn = 1 and and divide the interval [0, 1] into subintervals [x0, x1], [x1, x2], . . . , [xn−1, xn]. In each subinterval [xk−1, xk] there is a rational point qk, where f (qk) = 1, and also an irrational point rk, where f (rk) = 0. We approximate the Riemann integral from above by the upper sum 1 and also approximate it from below by the lower sum 0. No matter how fine we take the partition of [0, 1], the upper sum is always 1 and the lower sum is always 0. Since these two do not converge to a common value as the partition becomes finer, the Riemann integral is not defined.

Example 3. Consider the function f (x) =

∞, if x = 0, 0, if x 6= 0.

Every simple function which lies between 0 and f is of the form h(x) =

y, if x = 0, 0, if x 6= 0.

for some y ∈ [0, ∞), and thus has Lebesgue integral

Z

R

hdµ0 = yµ({0}).

It follows that RRf dµ0 = 0. Now consider the Riemann integral R−∞ f (x)dx, which for this function f is the same as the Riemann integralR−11 f (x)dx. When we partition [−1, 1] into subintervals, one of these will contain the point 0, and when we compute the upper approximating sum for R−11 f (x)dx, this point will contribute ∞ times the length of the subinterval containing it. Thus the upper approximating sum is ∞. On the other hand, the lower approximating sum is 0, and again the Riemann integral does not exist.

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The Lebesgue integral has all linearity and comparison properties one would expect of an integral. In particular, for any two functions f and g and any real constant c,

Z

R

(f + g)dµ0 =

Z

R

f dµ0 +

Z

R

gdµ0,

Z

R

cf dµ0 = c

Z

R

f dµ0,

Z

R

f dµ0

Z

R

gdµ0, when f (x) ≤ g(x)

Z

A∪B

f dµ0 =

Z

A

f dµ0+

Z

B

f dµ0.

There are three convergence theorems satisfied by the Lebesgue integral.

In each of these the situation is that there is a sequence of functions fn, n = 1, 2, . . . converging pointwise to a limiting function f . Pointwise convergence just means that

n→∞lim fn(x) = f (x) for every x ∈ R.

There are no such theorems for the Riemann integral, because the Riemann integral of the limiting function f is too often not defined. Before we state the theorems, we given two examples of pointwise convergence which arise in probability theory.

Example 4. Consider a sequence of normal densities, each with variance 1 and the n-th having mean n:

fn(x) = 1

√2πexp −(x − n)2 2

!

.

These converge pointwise to the zero function. We have RRfn0 = 1 for every n but RRf dµ)= 0.

Example 5. Consider a sequence of normal densities, each with mean 0 and the n-th having variance 1/n:

fn(x) = n

√2π exp − x2 2n−1

!

. These converge pointwise to the function

f (x) =

∞, if x = 0, 0, if x 6= 0.

We have RRfn0 = 1 for every n but RRf dµ0 = 0.

Theorem 3 (Fatous Lemma) Let fn, n = 1, 2, . . . be a sequence of nonnegative functions converging pointwise to a function f . Then

Z

R

f dµ0 ≤ lim inf

n→∞

Z

R

fn0.

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The key assumption in Fatou’s Lemma is that all the functions take only non- negative values. Fatou’s Lemma does not assume much but it is is not very satisfying because it does not conclude that

Z

R

f dµ0 = lim

n→∞

Z

R

fn0,

There are two sets of assumptions which permit this stronger conclusion.

Theorem 4 (Monotone Convergence Theorem) Let fn, n = 1, 2, . . . be a se- quence of functions converging pointwise to a function f . Assume that

0 ≤ f1(x) ≤ f2(x) ≤ · · · for every x ∈ R.

Then

Z

R

f dµ0 = lim

n→∞

Z

R

fn0, where both sides are allowed to be ∞.

Theorem 5 (Dominated Convergence Theorem) Let fn, n = 1, 2, . . . be a se- quence of functions converging pointwise to a function f . Assume that there is a nonnegative integrable function g (i.e., RRgdµ0 < ∞) such that

|fn(x)| ≤ g(x) for every x ∈ R for every n.

Then

Z

R

f dµ0 = lim

n→∞

Z

R

fn0, and both sides will be finite.

2.4 Related results in probability theory

Theorem 6 (Bounded Convergence Theorem) Suppose that Xnconverges to X in probability and that there exists a constant M such that P (|Xn| ≤ M ) = 1.

Then E(Xn) → E(X).

Proof. Let {xi} be a partition of R such that FX is continuous at each xi. Then

X

i

xiP {xi < Xn ≤ xi+1} ≤ E(Xn) ≤X

i

xi+1P {xi < Xn ≤ xi+1} and taking limits we have

X

i

xiP {xi < Xn≤ xi+1} ≤ limE(Xn)

≤ limE(Xn) ≤X

i

xi+1P {xi < Xn ≤ xi+1}.

As max |xi+1− xi| → 0, the left and right sides converges to E(X) giving the theorem.

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Theorem 7 (Monotone Convergence Theorem) Suppose 0 ≤ Xn ≤ X and Xn converges to X in probability. Then limn→∞E(Xn) = E(X).

Proof. For M > 0

E(X) ≥ E(Xn) ≥ E(Xn∧ M ) → E(X ∧ M )

where the convergence on the right follows from the bounded convergence the- orem. It follows that

E(X^M ) ≤ lim inf

n→E(Xn) ≤ lim sup

n→

E(Xn) ≤ E(X).

Theorem 8 (Dominated Convergence Theorem) Assume Xn and Yn converge to X and Y , respectively, in probability. Also, |Xn| ≤ Ynand E(Yn) → E(Y ) <

∞. Then limn→∞E(Xn) = E(X).

Its proof follows from Fatou Lemma.

### 3 Mode of Convergence

On Ω there is defined a sequence of real-valued functions X1(w), X2(w), . . . which are random variables in the sense of the following definition.

Definition A function X(w) defined on Ω is called a random variable if for every Borel set B in the real line R, the set is {w : X(w) ∈ B} is in F . (X(w) is a measurable function on (Ω, F ). )

3.1 Convergence in Distribution

Suppose we flip a fair coin 400 times and want to find out the probability of getting heads between 190 and 210. A standard practice is to invoke the Central Limit Theorem to get an approximation of the above probability. Let S400 denote the number of heads in the 400 flips. For this particular problem, our major concern is P (190 ≤ S400 ≤ 210) or whether this probability can be approximated well by P (−1.05 ≤ Z ≤ 1.05). Here Z is a standard normal ran- dom variable. In this example, we need the concept of converges in distribution.

Consider distribution functions F1(·), F2(·), . . . and F (·). Let X1, X2, . . . and X denote random variables (not necessarily on a common probability space) hav- ing these distributions, respectively. We say that Xn converges in distribution (or in law) to X if

n→∞lim Fn(t) = F (t), for all t which are continuity points of F .

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This is written Xn → X or Xd n → X or FL n → F . What are convergent herew are not the values of the random variables themselves, but the probabilities with which the random variables assume certain values. If Xn

→ X, then thed

distribution of Xn can be well approximated for large n by the distribution of X. This observation is extremely useful since FX is often easier to compute than FXn.

In general, we would like to say that the distribution of the random vari- ables Xn converes to the distribution of X if Fn(x) = P (Xn < x) → F (x) = P (X < x) for every x ∈ R. But this is a bit too strong. We now use an example to illustrate why we require the convergence only occurs at the continuity points of F ? Consider random variables Xn which take values 1 − n−1 or 1 + n−1 with probabilities 1/2. Heuristically, we would want the values of Xnto be more and more concentrated about 1. Note that the distribution of Xn is

Fn(x) =

0, x < 1 − n−1

1/2, 1 − n−1 ≤ x < 1 + n−1 1, x ≥ 1 + n−1.

By calculation, we have Fn(x) → F(x) as n → ∞ where

F(x) =

0, x < 1 1/2, x = 1 1, 1 < x.

On the other hand, for the random variable X taking value 1 with probability 1. The distribution of X is

F (x) =

0, x < 1 1, x ≥ 1.

Apparently, not much should be assumed about what happens for x at a dis- continuity point of F (x). Therefore, we can only consider convergence in dis- tribution at continuity points of F . Read Example 14.3-2(pp467) of Bishop, Feinberg and Holland (1975) for direct verification that Fn → F . Anotherw important tool for establishing convergence in distribution is to use moment- generating function or characteristic function. Read Example 14.3-3(pp467) of Bishop, Feinberg and Holland (1975). In later section, we will use this tool to prove the central limit theorem (Chung[1974], Theorem 6.4.4).

When we talk about convergence in distribution, w never come into the picture. As an example, flip a fair coin once. Let X = 1 if we get head and X = 0, otherwise. On the other hand, set Y = 1 − X. It is obvious that X

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and Y have the same distribution. As a remark, the random variable X is a function of w but we can never observe w.

3.2 Convergence with Probability 1

Next, we discuss convergence with probability 1 (or strongly, almost surely, al- most everywhere, etc.) which is closely related to the convergence of sequences of functions in advanced calculus. This criterion of convergence is of partic- ular importance in the probability limit theorems known as the laws of large numbers. This is defined in terms of the entire sequence of random variables X1, X2, . . . , Xn, . . .. Regarding such a sequence as a new random variable with realized value x1, x2, . . . , xn, . . ., we may say that this realized sequence either does or does not converge in the ordinary sense to a limit x. If the probability that it does so is unity, then we say that Xn → X almost certainly. Consider random variables X1, X2, · · · and X, we say that Xn converges with probability 1 (or almost surely) to X if

P (w : lim

n→∞Xn(w) = X(w)) = 1.

This is written Xn wp1→ X, n → ∞. To be better understanding this convergence, we give the following equivalent condition:

n→∞lim P (|Xm− Xn| < , for all m ≥ n) = 1, for every  > 0.

Suppose we have to deal with questions of convergence when no limit is in evidence. For convergence almost surely, this is immediately reducible to the numerical case where the Cauchy criterion is applicable. Specifically, {Xn} converges a.s. if and only if there exists a null set N such that for every w ∈ Ω − N and every  > 0, there exists m(w, ) such that

n0 > n ≥ m(w, ) → |Xn(w) − Xn0(w)| ≤ .

Or, for any positive  and η, there is an n0 such that

P {|Xn− Xm| >  for at least one m ≥ n} < η

for all n ≥ n0. As almost surely convergence depends on the simultaneous behavior of Xn for all n ≥ n0, it is obviously more difficult to handle, but the following sufficient criterion is useful. If Pn=1E{|Xn− X|p} < ∞ for some p > 0, then Xn → X almost surely. This criterion follows from the observation:

P (|Xm− X| >  for some m ≥ n) = P (∪m=n{|Xm− X| > })

X

m=n

P (|Xm− X| > ).

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3.2.1 Consistency of the Empirical Distribution Function

Let X1, . . . , Xn be independent identically distributed random variables on R with distribution function F (x) = P (X ≤ x). The nonparametric maximum- likelihood estimate of F is the sample distribution function or empirical distri- bution function defined as

Fn(x) = 1 n

n

X

i=1

I[Xi,∞)(x).

Thus, Fn(x) is the proportion of the observations that fall less than or equal to x.

For each fixed x, the strong law of large numbers implies that Fn(x)−→ F (x),a.s.

because we may consider I[Xi,∞)(x) as i.i.d. random variables with mean F (x).

Thus, Fn(x) is a strongly consistent estimate of F (x) for every x.

The following corollary improves on this observation in two ways. First, the set of probability one on which convergence takes place may be chosen to be independent of x. Second, the convergence is uniform in x. This assertion, that the empirical distribution function converges uniformly almost surely to the true distribution function, is known as the Glivenko-Cantelli Theorem.

COROLLARY. P {supx|Fn(x) − F (x)| → 0} = 1.

Proof. Let ε > 0. Find an integer k > 1/ε and numbers −∞ = x0 < x1 ≤ x2 ≤ · · · ≤ xk−1 < xk= ∞, such that

F (xj) ≤ j/k ≤ F (xj)

for j = 1, . . . , k − 1. [F (xj ) may be considered notation for P (X < xj).] Note that if xj−1 < xj then F (xj ) − F (xj−1) ≤ ε. From the strong law of large numbers, Fn(xj)−→ F (xa.s. j) and Fn(xj )−→ F (xa.s. j ) for j = 1, . . . , k − 1. Hence,

4n= max(|Fn(xj) − F (xj)|, |Fn(xj) − F (xj)|, j = 1, . . . , k − 1)−→ 0.a.s.

Let x be arbitrary and find j such that xj−1 < x ≤ xj. Then, Fn(x) − F (x) ≤ Fn(xj) − F (xj−1) ≤ Fn(xj) − F (xj) + , and

Fn(x) − F (x) ≥ Fn(xj−1) − F (xj ) ≥ Fn(xj − 1) − F (xj−1) − .

This implies that

sup

x

|Fn(x) − F (x)| ≤ 4n+ −→ .a.s.

Since this holds for all  > 0, the corollary follows.

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3.2.2 Law of Large Numbers

The weak (strong) law of large numbers states the sample mean is a weakly (strongly) consistent estimate of the population mean. The weak law of large numbers says that if X1, . . . , Xn are i.i.d. random variables with finite first moment, µ, then for every  > 0 we have

P (| ¯Xn− µ| > ) → 0

as n → ∞. The argument of using Chebyschev inequality with finite second moment shows that

P (| ˆXn− µ| > ) → 0

at rate 1/n. On the other hand, we can show that ˆXn converges to µ weakly (strongly) as long as E|X| < ∞.

3.3 Convergence in Probability

We say that Xn converges in probability to X as n → ∞ if, for any positive ,

n→∞lim P (w : |Xn(w) − X(w)| > ) = 0.

This is written Xn

→ X, as n → ∞. A necessary and sufficient condition forP

such convergence is that for any positive  and η there is an n0 such that P (w : |Xn(w) − X(w)| > ) < η for all n ≥ n0.

A numerical constant c can always be viewed as a degenerate random variable C whose distribution has all of its probability concentrated on the single value c.

As an example, the weak law of large numbers states that the random variable sample mean converges in probability to a population mean (a constant).

Now we try to use the following theorem and the example to illustrate the difference between converegence with probability 1 and convergence in probabil- ity. For convergence in probability, one needs for every  > 0 that the probability that Xn is within  of X tends to one. For convergence almost surely, one needs for every  > 0 that the probability that Xn stays within  of X for all k ≥ n tends to one as n tends to infinity.

Theorem 9 The sequence {Xn} of random variables converges to a random variable X with probability 1 if and only if

n→∞lim P {∪m=n(|Xm− X| ≥ )} = 0 for every  > 0.

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By the above theorem, convergence in probability is weaker than con- vergence with probability 1. The following example is used to illustrate the difference.

Example 6. Let Ω = [0, 1], and let S be the class of all Borel sets on Ω. Let P be the Lebesgue measure. For any positive integer n, choose integer m with 2m ≤ n < 2m+1. Clearly, n → ∞ if and only if m → ∞. We can write n ≥ 1 as n = 2m+ k, k = 0, 1, . . . , 2m− 1. Let us define Xn on Ω by

Xn(w) =

1 if w ∈h2km,k+12m

i, 0 otherwise,

if n = 2m+ k. Then Xn is a random variable which satisfies P {|Xn(w)| ≥ } =

1

2m if 0 <  < 1, 0 if  ≥ 1, so that Xn

→ 0. However, XP n does not converge to 0 with probability 1. In fact, for any w ∈ [0, 1], there are an infinite number of intervals of the form [k/2m, (k + 1)/2m] which contain w. Such a sequence of intervals depends on w. Let us denote it by

("

k

2m,k + 1 2m

#

, m = 1, 2, . . .

)

,

and let nm = 2m+ km. Then Xnm(w) = 1, but Xn(w) = 0 if n 6= nm. It follows that {Xn} does not converge at w. Since w is arbitrary, Xn does not converge with probability 1 to any random variable.

3.3.1 Borel-Cantelli Lemma

First, we give an example to illustrate the difference between convergence in probability and convergence in distribution. Consider {Xn} where Xn is uni- formly distributed on the set of points {1/n, 2/n, . . . , 1}. It can be shown easily that Xn→ X where X is uniformly distributed over (0, 1). Can we answer theL question whether Xn → X?P

Next, we give the Borel-Cantelli Lemma and the concept of infinitely of- ten which are often used in proving strong law of large number. For events Aj, j = 0, 1, . . ., the event {Aj i.o.} (read Aj infinitely often), stands for the event that infinitely many Aj occur.

THE BOREL-CANTELLI LEMMA. IfPj=1P (Aj) < ∞, then P {Aj i.o.} = 0. Conversely, if the Ajare independent andPj=1P (Aj) = ∞, then P {Aj i.o.} = 1.

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Proof. (The general half) If infinitely many of the Aj occur, then for all n, at least one Aj with j ≥ n occurs. Hence,

P {Aj i.o.} ≤ P

[

j=n

Aj

X

j=n

P (Aj) → 0.

The proof of the converse can be found in standard probability textbook.

A typical example of the use of the Borel-Cantelli Lemma occurs in coin tossing. Let X1, X2, . . . be a sequence of independent Bernoulli trials with probability of success on the nth trial equal to pn. What is the probability of an infinite number of successes? Or, equivalently, what is P {Xn = 1 i.o.}?

From the Borel-Cantelli Lemma and its converse, this probability is zero or one depending on whether Ppn < ∞ or not. If pn = 1/n2, for example, then P {Xn= 1 i.o.} = 0. If pn = 1/n, then P {Xn= 1 i.o.} = 1.

The Borel-Cantelli Lemma is useful in dealing with problems involving almost sure convergence because Xn−→ X is equivalent toa.s.

P {|Xn− X| >  i.o.} = 0, for all  > 0.

3.4 Convergence in rth Mean

We say that Xn converges in rth mean to X if

n→∞lim E|Xn− X|r = 0.

This is written Xn rth→ X, n → ∞. We say that X is dominated by Y if |X| ≤ Y almost surely, and that the sequence {Xn} is dominated by Y iff this is true for each Xn with the same Y . We say that X or {Xn} is uniformly bounded iff the Y above may be taken to be a constant. Observe that

E|Xn−X|r = E|Xn−X|r1{|Xn−X|<}+E|Xn−X|r1{|Xn−X|>}≤ r+EYr1{|Xn−X|>}. We then conclude that Xn rth→ X if Xn → X and {XP n} is dominated by some Y that belongs to Lp.

We now use a Chebyshev type of “weak laws of large numbers” to demon- strate a method for determining the large sample behavior of linear combination of random variables.

Theorem (Chebyshev). Let X1, X2, . . . be uncorrelated with means µ1, µ2, . . . and variances σ12, σ22, . . .. IfPni=1σi2 = o(n2), n → ∞, then

1 n

n

X

i=1

Xi− 1 n

n

X

i=1

µi

→ 0.P

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If the bootstrap distribution of a statistic shows a normal shape and small bias, we can get a confidence interval for the parameter by using the boot- strap standard error and

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