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Tathagata Banerjee

Other affiliations: University of Calcutta
Bio: Tathagata Banerjee is an academic researcher from Indian Institute of Management Ahmedabad. The author has contributed to research in topics: Regression analysis & Estimator. The author has an hindex of 10, co-authored 34 publications receiving 300 citations. Previous affiliations of Tathagata Banerjee include University of Calcutta.

Papers
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TL;DR: This work investigates the performances of ten commonly used intervals of a single binomial proportion, in the light of two criteria, viz., coverage and expected length of the interval.
Abstract: In testing for non-inferiority or superiority in a single arm study, the confidence interval of a single binomial proportion is frequently used. A number of such intervals are proposed in the literature and implemented in standard software packages. Unfortunately, use of different intervals leads to conflicting conclusions. Practitioners thus face a serious dilemma in deciding which one to depend on. Is there a way to resolve this dilemma? We address this question by investigating the performances of ten commonly used intervals of a single binomial proportion, in the light of two criteria, viz., coverage and expected length of the interval.

9 citations

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TL;DR: In this article, the authors focus on the question of specification of measurement error distribution and the distribution of true predictors in generalized linear models when the predictors are subject to measurement errors.
Abstract: This paper focuses on the question of specification of measurement error distribution and the distribution of true predictors in generalized linear models when the predictors are subject to measurement errors. The standard measurement error model typically assumes that the measurement error distribution and the distribution of covariates unobservable in the main study are normal. To make the model flexible enough we, instead, assume that the measurement error distribution is multivariate t and the distribution of true covariates is a finite mixture of normal densities. Likelihood–based method is developed to estimate the regression parameters. However, direct maximization of the marginal likelihood is numerically difficult. Thus as an alternative to it we apply the EM algorithm. This makes the computation of likelihood estimates feasible. The performance of the proposed model is investigated by simulation study.

7 citations

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TL;DR: In this article, a model-based predictive estimator is proposed for the population proportions of a polychotomous response variable, based on a sample from the population and on auxiliary variables, whose values are known for the entire population.
Abstract: . A model-based predictive estimator is proposed for the population proportions of a polychotomous response variable, based on a sample from the population and on auxiliary variables, whose values are known for the entire population. The responses for the non-sample units are predicted using a multinomial logit model, which is a parametric function of the auxiliary variables. A bootstrap estimator is proposed for the variance of the predictive estimator, its consistency is proved and its small sample performance is compared with that of an analytical estimator. The proposed predictive estimator is compared with other available estimators, including model-assisted ones, both in a simulation study involving different sampling designs and model mis-specification, and using real data from an opinion survey. The results indicate that the prediction approach appears to use auxiliary information more efficiently than the model-assisted approach.

5 citations

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TL;DR: If the diagnostic test is imperfect, the group testing is found to yield more precise estimate of the log-odds ratio than the individual testing, and Firth correction to the score function leads to a considerable improvement of the estimator.
Abstract: We consider the estimation of the prevalence of a rare disease, and the log-odds ratio for two specified groups of individuals from group testing data. For a low-prevalence disease, the maximum likelihood estimate of the log-odds ratio is severely biased. However, Firth correction to the score function leads to a considerable improvement of the estimator. Also, for a low-prevalence disease, if the diagnostic test is imperfect, the group testing is found to yield more precise estimate of the log-odds ratio than the individual testing.

5 citations

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TL;DR: The application of cadmium significantly reduced the mean level of GSH, however, this reduction in GSH was not due to additive genetic influences in the authors' sample.
Abstract: Previous studies have shown that Glutathione, a tripeptide found in blood, is involved in protecting against toxins. Glutathione levels are known to drop in response to cadmium. Using 15 twin pairs, we modeled the effect of cadmium on glutathione levels. The heritability of glutathione content was 91%. The application of cadmium significantly reduced the mean level of GSH. However, this reduction in GSH was not due to additive genetic influences in our sample.

4 citations


Cited by
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TL;DR: The saimie paper suggests how susceptible individuals could reduce their total intake of aluminium and suggests that although definite proof is still lacking, there is more than enough evidence to fuel further epidemiological investigation.
Abstract: The saimie paper suggests how susceptible individuals could reduce their total intake of aluminium. In presenting the cpidemiological evidence for a link betveen aluminium and Alzheimcr's, Nart'n suggests that although definite proof is still lacking, there is more than enough positixe evidence to fuel further epidemiological investigation. It states that such investigations might specificallx address the issue of the confounding cffect of silicon and an assessment of exposure to spccific

1,353 citations

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TL;DR: In this paper, a Bayesian method was proposed to account for measurement errors in linear regression of astronomical data. The method is based on deriving a likelihood function for the measured data, and focus on the case when the intrinsic distribution of the independent variables can be approximated using a mixture of Gaussian functions.
Abstract: I describe a Bayesian method to account for measurement errors in linear regression of astronomical data. The method allows for heteroscedastic and possibly correlated measurement errors and intrinsic scatter in the regression relationship. The method is based on deriving a likelihood function for the measured data, and I focus on the case when the intrinsic distribution of the independent variables can be approximated using a mixture of Gaussian functions. I generalize the method to incorporate multiple independent variables, nondetections, and selection effects (e.g., Malmquist bias). A Gibbs sampler is described for simulating random draws from the probability distribution of the parameters, given the observed data. I use simulation to compare the method with other common estimators. The simulations illustrate that the Gaussian mixture model outperforms other common estimators and can effectively give constraints on the regression parameters, even when the measurement errors dominate the observed scatter, source detection fraction is low, or the intrinsic distribution of the independent variables is not a mixture of Gaussian functions. I conclude by using this method to fit the X-ray spectral slope as a function of Eddington ratio using a sample of 39 z 0.8 radio-quiet quasars. I confirm the correlation seen by other authors between the radio-quiet quasar X-ray spectral slope and the Eddington ratio, where the X-ray spectral slope softens as the Eddington ratio increases. IDL routines are made available for performing the regression.

1,264 citations

Journal ArticleDOI
TL;DR: This work discusses the practice of problem solving, testing hypotheses about statistical parameters, calculating and interpreting confidence limits, tolerance limits and prediction limits, and setting up and interpreting control charts.
Abstract: THE best adjective to describe this work is \"sweep11 ing.\" The range of subject matter is so broad that it can almost be described as containing everything except fuzzy set theory. Included are explicit discussions of the basics of probability (relegated to an appendix); the practice of problem solving; testing hypotheses about statistical parameters; calculating and interpreting confidence limits; tolerance limits and prediction limits; setting up and interpreting control charts; design of experiments; analysis of variance; line and surface fitting; and maximum likelihood procedures. If you can think of something that is not in this list, then it probably means I have overlooked it.

309 citations

Journal ArticleDOI
TL;DR: It is demonstrated that MOABS outperforms other leading algorithms, such as Fisher’s exact test and BSmooth, and can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq.
Abstract: Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher’s exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.

246 citations