Constrained Bayes and Empirical Bayes Estimation with Balanced Loss Functions

This article develops constrained Bayes and empirical Bayes estimators under balanced loss functions. In the normal-normal example, estimators of the mean squared errors of the EB and constrained EB estimators are provided which are correct asymptotically up to O(m ^?1), m denoting the number of strata.     

Two normal approximations to the binomial distribution

Two new normal approximations are proposed for the cumulative binomial distribution when the mean-is reasonably large. Their adequacy is compared with that of certain well-known approximations- The first is recommended for its simplicity and accuracy relative to the standard and Gram-Charlier approximations. The second Is shown to be more accurate than all known approximations for a certain range of the probability of success.     

Nonparametric empirical bayes estimation of certain funtionals

For estimating functionals of the form ∫∫φ(x,y)dF(x) dF(y), nonparametric empirical Bayes estimators are developed which are competitors of the classical U-statistics. Asymptotic optimality of the proposed estimators is proved     

Sequential shrinkage estimation of independent normal means with unkown variances

The Paper considers estimation of the p(> 3)-variate normal mean when the variance-covariance matrix is diagonal with unknown diagonal elements. A class of James-Stein estimators is developed, and is compared with the sample mean under an empirical minimax stopping rule. Asymptotic risk expansions are provided for both the sequential sample mean and the sequential James-Stein estimators. It is shown that the James-Stein estimators dominate the sample mean in a certain asymptotic sense.     

Maximum likelihood estimation foe two parameter exponentials under type I censoring

The paper considers maximum likelihood estimation of the location parameters and the failure rates of two parameter exponentials under type I censoring. Both the one and two sample cases are considered. It turns out that by debiasing the maximum likelihood estimators of the location parameters, one can achieve asymptotically 50% mean squared error reduction.     

ADJUSTING RESIDUALS IN ESTIMATION OF DISPERSION EFFECTS

This paper studies a method of adjusting the ordinary least squares residuals, when estimating and comparing dispersions, at various levels of factors in a replicated factorial experiment. Using a general dispersion model, theoretical results demonstrate the benefits of the method of adjusting residuals. An illustrative example is included.     

A flexible class of models for data arising from a ‘thorough QT/QTc study’

The standard methods for analyzing data arising from a ‘thorough QT/QTc study’ are based on multivariate normal models with common variance structure for both drug and placebo. Such modeling assumptions may be violated and when the sample sizes are small, the statistical inference can be sensitive to such stringent assumptions. This article proposes a flexible class of parametric models to address the above‐mentioned limitations of the currently used models. A Bayesian methodology is used for data analysis and models are compared using the deviance information criteria. Superior performance of the proposed models over the current models is illustrated through a real dataset obtained from a GlaxoSmithKline (GSK) conducted ‘thorough QT/QTc study’. Copyright © 2010 John Wiley & Sons, Ltd.     

A Semiparametric Bayesian Approach to Multivariate Longitudinal Data

We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees prior distribution. Usually, the distribution of random effects in a longitudinal data model is assumed to be Gaussian. However, the normality assumption may be suspect, particularly if the estimated longitudinal trajectory parameters exhibit multimodality and skewness. In this paper we propose a mixture of Polya trees prior density to address the limitations of the parametric random effects distribution. We illustrate the methodology by analyzing data from a recent HIV-AIDS study.     

A SEMIPARAMETRIC BAYESIAN APPROACH TO NETWORK MODELLING USING DIRICHLET PROCESS PRIOR DISTRIBUTIONS

This paper considers the use of Dirichlet process prior distributions in the statistical analysis of network data. Dirichlet process prior distributions have the advantages of avoiding the parametric specifications for distributions, which are rarely known, and of facilitating a clustering effect, which is often applicable to network nodes. The approach is highlighted for two network models and is conveniently implemented using WinBUGS software.