JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS)
Year:2006 | Volume:5 | Issue:1-2|Papers Count:6

This article presents a test based on quadratic form using Type-2 with replacement-censored sample for testing exponentiality against weibull IFR/DFR alternative. The percentile points and powers are simulated. The proposed test is compared with that of Bain and Engelhardt (1986) test. An example based on Type-2 censoring is also discussed.

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure is illustrated by analyzing a data set which has previously been analyzed by various authors. It is shown that our model is more precise and computationally less taxing.

In this paper, we first show that Renyi distance between any member of a parametric family and its perturbations is proportional to its Fisher information. We, then, prove some relations between the Renyi distance of two distributions and the Fisher information of their exponentially twisted family of densities. Finally, we show that the partial ordering of families induced by Renyi distance is the same as that induced by Fisher information.

Recently Habibi et al. (2006) defined a pre-experimental criterion for the potential strength of evidence provided by an experiment, based on Kullback-Leibler distance. In this paper, we investigate the potential statistical evidence in an experiment in terms of Renyi distance and compare the potential statistical evidence in lower (upper) record values with that in the same number of iid observations from the same parent distribution.

Let {Xn, n³1} be a strictly stationary sequence of negatively associated random variables, with common continuous and bounded distribution function F. In this paper, we consider the estimation of the two-dimensional distribution function of (X1, Xk+1) based on histogram type estimators as well as the estimation of the covariance function of the limit empirical process induced by the sequence {Xn, n³1}. Then, we derive uniform strong convergence rates for two-dimensional distribution function of (X1,Xk+1) without any condition on the covariance structure of the variables. Finally, assuming a convenient decrease rate of the covariance’s                                         Cov (X1,Xn+1), n³1we introduce uniform strong convergence rate for covariance function of the limit empirical process.

In this paper, we obtain some Rosenthal’s type inequalities for negatively orthant dependent (NOD) random variables.