Tathagata Banerjee

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.

Estimation of Poisson mean with under-reported counts: a double sampling approach

Abstract: Count data arising in various fields of applications are often under‐reported. Ignoring undercount naturally leads to biased estimators and inaccurate confidence intervals. In the presence of undercount, in this paper, we develop likelihood‐based methodologies for estimation of mean using validation data. The asymptotic distributions of the competing estimators of the mean are derived. The impact of ignoring undercount on the coverage and length of the confidence intervals is investigated using extensive numerical studies. Finally an analysis of heat mortality data is presented.

Identifying Cross-Reactive Pollen Allergens : An Empirical Approach

Abstract: Twenty airborne pollen grains and fungal spores prevalent in the air during February to June in and around Kolkata, India were found to have caused most of the allergy related respiratory illness among 100 atopic paients who visited the allergy clinic of the Institute of Child Health, Kolkata. They were subjected to the skin prick tests(SPT) using extracts from the allergens selected in accordance with the patient's history.Strongly cross­reactive allergens are expected to show similar reactions with respect to wheal diameters when applied to a given patient. An exploratory data analysis of wheal diameters is proposed to discover the groups of cross reactive allergens.Cluster analysis of wheal diameters is carried out to identify the groups of cross reactive allergens. It is well known that the same data set may produce altogether different clusters if different clustering methods are applied or different measures of dissimilarities are used. As such the clusters produceed by a single method and a single ...

Prediction of Finite Population Proportion When Responses Are Misclassified

Abstract: We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.

Methodology for Genetic Studies of Twins and Families

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

Some Aspects of Measurement Error in Linear Regression of Astronomical Data

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.

Experiments: Planning, Analysis, and Parameter Design Optimization

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.

MOABS: model based analysis of bisulfite sequencing data

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/.