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- Krishna K. Saha, Sudhir Paul
- Biometrics
- 2005

We derive a first-order bias-corrected maximum likelihood estimator for the negative binomial dispersion parameter. This estimator is compared, in terms of bias and efficiency, with the maximum likelihood estimator investigated by Piegorsch (1990, Biometrics46, 863-867), the moment and the maximum extended quasi-likelihood estimators investigated by Clark… (More)

- Krishna K. Saha, Roger Bilisoly
- Computational Statistics & Data Analysis
- 2009

- Krishna K. Saha, Sudhir Paul
- Statistics in medicine
- 2005

A popular model to analyse over/under-dispersed proportions is to assume the extended beta-binomial model with dispersion (intraclass correlation) parameter phi and then to estimate this parameter by maximum likelihood. However, it is well known that maximum likelihood estimate (MLE) may be biased when the sample size n or the total Fisher information is… (More)

- Krishna K. Saha
- Biometrical journal. Biometrische Zeitschrift
- 2013

This paper focuses on the development and study of the confidence interval procedures for mean difference between two treatments in the analysis of over-dispersed count data in order to measure the efficacy of the experimental treatment over the standard treatment in clinical trials. In this study, two simple methods are proposed. One is based on a sandwich… (More)

- Krishna K. Saha
- Statistics in medicine
- 2011

The over-dispersion parameter is an important and versatile measure in the analysis of one-way layout of count data in biological studies. For example, it is commonly used as an inverse measure of aggregation in biological count data. Its estimation from finite data sets is a recognized challenge. Many simulation studies have examined the bias and… (More)

Here the simulation design is the same as that in scenario 1 described in the main text with two partially missing variables X1 and X2. Missing data were created by following the two non-ignorable mechanisms, 1) logit{pr(Rk = 1|X1, X2, Y, Z)} = 0.25Y + 0.25Z + X2 + X1 and 2) logit{pr(Rk = 1|X1, X2, Y, Z)} = 0.75 + Y + 0.25Z −X1 + X2, for k = 1, 2. Although… (More)

- Krishna K. Saha
- Statistics in medicine
- 2012

The intraclass correlation in binary outcome data sampled from clusters is an important and versatile measure in many biological and biomedical investigations. Properties of the different estimators of the intraclass correlation based on the parametric, semi-parametric, and nonparametric approaches have been studied extensively, mainly in terms of bias and… (More)

- Krishna K. Saha, Daniel Miller, Suojin Wang
- The international journal of biostatistics
- 2016

Interval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some… (More)

- Samiran Sinha, Krishna K. Saha, Suojin Wang
- Biometrics
- 2014

Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random.… (More)

- Krishna K. Saha
- Biometrical journal. Biometrische Zeitschrift
- 2014

Over/underdispersed count data arise in many biostatistical practices in which a number of different treatment groups are compared in an experiment. In the analysis of several treatment groups of such count data, a very common statistical inference problem is to test whether these data come from the same population. The usual practice for testing… (More)