The formal posterior of a standard flat prior in MANOVA is incoherent

Summary A standard improper prior for the parameters of a MANOVA model is shown to yield an inference that is incoherent in the sense of Heath and Sudderth. The proof of incoherence is based on the fact that the formal Bayes estimate, sayδ ₀ , of the covariance matrix based on the improper prior and a certain bounded loss function is uniformly inadmissible in that there is another estimatorδ _l and an ɛ>0 such that the risk functions satisfyR(δ _l ,Σ)⩽R δ ₀ ,Σ)−ε for all values of the covariance matrix Σ. The estimatorδ _I is formal Bayes for an alternative improper prior which leads to a coherent inference. Research supported by National Science Foundation grants DMS-89-22607 (for Eaton) and DMS-9123358 (for Sudderth).     

n元函数的可微性

本文从分析、代数、几何诸方面对分析学中的重要概念——可微进行了刻划,得到了可微的几个等价条件.     

A NEW FAMILY OF NON-NEGATIVE DISTRIBUTIONS

We introduce a new, flexible family of distributions for non‐negative data, defined by means of a quantile function. We describe some properties of this family, and discuss several methods for estimating the parameters. The distribution is applied to an example from environmental engineering.     

An efficiency result for the empirical characteristic function in stationary time-series models

It is shown under general conditions that arbitrarily high asymptotic efficiencies can be obtained when the parameters of a stationary time series are estimated by fitting the characteristic functions of the process to their empirical versions. A consistency and a central limit result are also given.     

Goodness-of-fit tests for progressively Type-II censored data from location–scale distributions

In this article, we propose several goodness-of-fit methods for location–scale families of distributions under progressively Type-II censored data. The new tests are based on order statistics and sample spacings. We assess the performance of the proposed tests for the normal and Gumbel models against several alternatives by means of Monte Carlo simulations. It has been observed that the proposed tests are quite powerful in comparison with an existing goodness-of-fit test proposed for progressively Type-II censored data by Balakrishnan et al. [Goodness-of-fit tests based on spacings for progressively Type-II censored data from a general location–scale distribution, IEEE Trans. Reliab. 53 (2004), pp. 349–356]. Finally, we illustrate the proposed goodness-of-fit tests using two real data from reliability literature.     

Estimating a uniform distribution when data are measured with a normal additive error with unknown variance

The problem of estimating the width of a symmetric uniform distribution on the line together with the error variance, when data are measured with normal additive error, is considered. The main purpose is to analyse the maximum-likelihood (ML) estimator and to compare it with the moment-method estimator. It is shown that this two-parameter model is regular so that the ML estimator is asymptotically efficient. Necessary and sufficient conditions are given for the existence of the ML estimator. As numerical problems are known to frequently occur while computing the ML estimator in this model, useful suggestions for computing the ML estimator are also given.     

The decreasing percentile residual life ageing notion

Earlier researchers have studied some aspects of the classes of distribution functions with decreasing α-percentile residual life (DPRL(α)), 0<α<1. The purpose of this paper is to note some further properties of these classes, and to initiate a theory of non-parametric statistical estimation of DPRL(α) functions. Specifically, the close relationship between the DPRL(α) and the increasing failure rate ageing notions is studied. Other close relationships, between the DPRL(α) ageing notions and the percentile residual life stochastic orders, are described, and further properties of the above classes of distributions are derived. Finally, we introduce an estimator of the percentile residual life function, under the condition that it decreases, and we prove its strongly uniform consistency.     

An Improvement of Poisson Approximation for Sums of Dependent Bernoulli Random Variables

This article uses the Stein-Chen method to obtain new non uniform bounds on the error of the cumulative distribution function of sums of dependent Bernoulli random variables and the Poisson cumulative distribution function. The bounds obtained in the present study are sharper than those reported in Teerapabolarn and Neammanee (2006 Teerapabolarn, K., Neammanee, K. (2006). Poisson approximation for sums of dependent Bernoulli random variables. Acta Mathematica Academiae Paedagogicae Nyiregyhaziensis. 22:87–99. [Google Scholar]). Examples are provided to illustrate applications of the obtained results.     

On the functional relationship between entropy and variance with related applications

The functional relationship between entropy and variance is investigated for some well-known distributions. The distributions considered here are the reparameterized versions of the original forms. Such a reparameterization is necessary as in each case we have a common variance. The related graphs of entropy as a function of variance are used for certain comparisons. Further, within the class of distributions having a common variance, a measure of affinity between these distributions is proposed using entropy. A few aspects of the sampling distributions of an estimator of entropy, when the samples are either from the normal or from the exponential distributions, are discussed with a view to possible applications in the testing of hypotheses for related parameters