Debashis Kushary

Rutgers, The State University of New Jersey, Camden · Mathematical Sciences

Purpose: The New Jersey Cancer Education and Early Detection (NJCEED) program provides breast cancer screening to low income, uninsured, and underinsured women. The purpose of this study was to evaluate the effectiveness of the NJCEED program by considering stage at diagnosis for women enrolled in NJCEED compared to women diagnosed in the state of...

Objectives: The National Breast and Cervical Cancer Early Detection Program provides free or low-cost screening to uninsured or underinsured women and has had positive results; however, only a few state programs have been evaluated. This study will provide a first snapshot of the effectiveness of the New Jersey program, by comparing stage at diagn...

Background: The rate of contralateral prophylactic mastectomy (CPM) in women with unilateral mastectomy is increasing with no plateau. Objectives: The aim of this study was to improve the understanding of patient- and tumor-related factors that influenced the choice of mastectomy with CPM as treatment for early-stage breast cancer at an academic...

Multistage sampling schemes for statistical inference are widespread. In particular, computing one or two sided confidence limits from two-stage samples has many applications. Typically, in a two stage sampling scheme, at the end of the first stage, the investigator carries out a statistical testing procedure to decide whether to move on to the sec...

A ranked set sampling procedure with unequal samples for positively skew distributions (RSSUS) is proposed and used to estimate the population mean. The estimators based on RSSUS are compared with the estimators based on ranked set sampling (RSS) and median ranked set sampling (MRSS) procedures. It is observed that the relative precisions of the es...

A ranked set sampling procedure with unequal samples and unequal replications (RSSUR) is proposed and used to estimate the population mean. This estimator is then compared with the estimators based on ranked set sampling with unequal samples and equal replications (RSSU), median ranked set sampling (MRSS) and ranked set sampling (RSS). It is shown...

This book gives a broad and up-to-date coverage of bootstrap methods, with numerous applied examples, developed in a coherent way with the necessary theoretical basis. Applications include stratified data; finite populations; censored and missing data; linear, nonlinear, and smooth regression models; classification; time series and spatial problems...

This paper develops a sequential decision-making model for assisting law enforcement officials in allocating resources during a crackdown operation on illicit drug markets. The sequential crackdown model (SCM) considers a probabilistic framework, where the probability of incarceration of a dealer and the probability of dealing are modeled as a func...

The notion of universal admissibility of estimators was introduced and developed by Hwang (1985) and Brown and Hwang (1989). In several models commonly used estimators of scale parameters are shown to be inadmissible under specified loss functions. Here we focus on the scale and location-scale invariant estimation of the scale parameter under the u...

The notion of universal admissibility of estimators was introduced and developed by Hwang [9] and Brown and Hwang [5]. In several models the maximum likelihood estimator is known to be inadmissible for various specified loss functions. In this paper we demonstrate that the maximum likelihood estimator is universally admissible in the following four...

We consider the problem of classifying single and multiple observations between two class tail contaminated models and derive minimax classification rules for the problems.

Let x1 ≤x2 ≤x3 … ≤xr be the r smallest observations out of n observations from a location-scale family with density where μ and σ are the location and the scale parameters respectively. The goal is to construct a prediction interval of the form for a location-scale invariant function, T(Y) = T(Y1, …, Ym), of m future observations from the same dist...

Restricted maximum likelihood estimators for inverse Gaussian parameters when sampling from Type I singly censored samples are considered. The properties of these estimators are studied using simulation and comparison to other methods of maximum likelihood estimation procedure is done.

Consider the problem of prediction in a change point regression model. That is, assume a simple linear regression model holds for all x (independent variable) less than [gamma]k, and a different simple linear regression model holds for x #62; [gamma]k. However, [gamma]k, the change point, is unknown but can be one of m possible values ([gamma]1 [le...

A simulation-based procedure is suggested for constructing prediction limits for Weibull populations. This procedure is based on maximum likelihood (ML) estimation. Although computation of the ML estimates and determination of a needed percentile via simulation require a computer, we assert that the proliferation of personal computers makes these p...

Assume independent random samples are drawn from two populations which are following exponential distributions with unknown location and scale parameters. We assume that the location parameters are ordered. It is also shown that the standard estimators to estimate the location parameters in the unrestricted case which uses information only from one...

Assume independent random samples are drawn from two populations which are exponentially distributed with unknown location parameters and a common known scale parameter. We want to estimate the maximum and the minimum of the unknowo location paremeters. In this paper several estimators are proposed which are better than the natural estimations in t...

Methods of constructing exact tolerance intervals (β-expectation and β-content) for independent observations are well known. For the case of dependent observations, obtaining exact results is not possible. In this article we provide an approximate method of constructing β-expectation tolerance intervals via a Taylor series expansion. Examples of in...

Assume independent random samples are drawn from K populations whose distributions are location, scale, or location-scale families. Let T 1 be an estimator which is admissible for the parameter corresponding to the first population. Next assume that the parameters are ordered. The question addressed is does T 1 remain admissible? For various specia...