• 沒有找到結果。

The Discrete Normal Distribution 1871

N/A
N/A
Protected

Academic year: 2022

Share "The Discrete Normal Distribution 1871"

Copied!
2
0
0

加載中.... (立即查看全文)

全文

(1)

The Discrete Normal Distribution 1871 D. Roy

On a Relation Between a Family of Distributions Attaining the Bhattacharyya Bound and That of Linear Combinations

of the Distributions from an Exponential Family 1885 H. Tanaka

SAMPLING THEORY

Optimum Allocation of Stratified Random Samples Designed for Multiple Mean Estimates and Multiple Observed

Variables 1897 V. Bosch and R. Wildner

INFERENCE

Estimation of Parameters of Mixed Failure Time Distribution

Based on an Extended Modified Sampling Scheme 1911 V. V. Dixit

Asymptotic Theory for the Multiscale Wavelet Density

Derivative Estimator 1925 T. Ochiai and K. Naito

A Class of Improved Heteroskedasticity-Consistent

Covariance Matrix Estimators 1951 F. Cribari-Neto and N. M. S. Galvdo

On Adaptive Transformation-Retransformation Estimate of

Conditional Spatial Median 1981 A. Gannoun, J. Saracco, A. Yuan, and G. E. Bonney

(continued on inside back cover)

(2)

Comparing Two Population Means and Variances: A

Parametric Robust Way 2013 T.-S. Tsou

RELIABILITY

Reliability Properties of Reversed Residual Lifetime 2031 A. K. Nanda, H. Singh, N. Misra, and P. Paul

SURVIVAL ANALYSIS An Estimator for the Survival Function on the Basis of

Observable Censoring Times 2043 U. Jensen and A. Narr

REGRESSION ANALYSIS

On Directional Dependence in a Regression Line 2053 M. V. Muddapur

TIME SERIES

Asymptotic Distribution of a Unit Root Process Under

Double Truncation 2059 E. Goldman and H. Tsurumi

GOODNESS-OF-FIT TESTS

\2-Type Goodness of Fit Test Based on Transformed

Empirical Processes for Location and Scale Families 2073 J. Graneri

參考文獻

相關文件

As an example of a situation where the mgf technique fails, consider sampling from a Cauchy distribution.. Thus, the sum of two independent Cauchy random variables is again a

Part (a) can be established based on the density formula for variable transforma- tions.. Part (b) can be established with the moment

The image of the set  is the region  shown in the ­plane, a parallelogram bounded by these four line segments.. The transformation maps the boundary of  to the boundary of

There is the Central Limit Theorem, which shows that, under mild conditions, the normal distribution can be used to approximate a large variety of distributions in large samplesb.

The random vector (X, Y ) defined above is called a discrete random vector because it has only a countable (in this case, finite) number of possible values.. The probabilities of

developed for multiple discrete random variables by an an extension of the ideas used for two discrete random variables.. Example

Assume the weights in the population are normally distributed with unknown mean θ and known standard deviation 20 pounds.. Suppose your prior distribution for θ is normal with mean

• Variance of the random variable X (or of its distribution) - second moment about the