A Conversation with Shoutir Kishore Chatterjee

Shoutir Kishore Chatterjee was born in Ranchi, a small hill station in India, on November 6, 1934. He received his B.Sc. in statistics from the Presidency College, Calcutta, in 1954, and M.Sc. and Ph.D. degrees in statistics from the University of Calcutta in 1956 and 1962, respectively. He was appointed a lecturer in the Department of Statistics, University of Calcutta, in 1960 and was a member of its faculty until his retirement as a professor in 1997. Indeed, from the 1970s he steered the teaching and research activities of the department for the next three decades. Professor Chatterjee was the National Lecturer in Statistics (1985--1986) of the University Grants Commission, India, the President of the Section of Statistics of the Indian Science Congress (1989) and an Emeritus Scientist (1997--2000) of the Council of Scientific and Industrial Research, India. Professor Chatterjee, affectionately known as SKC to his students and admirers, is a truly exceptional person who embodies the spirit of eternal India. He firmly believes that ``fulfillment in man's life does not come from amassing a lot of money, after the threshold of what is required for achieving a decent living is crossed. It does not come even from peer recognition for intellectual achievements. Of course, one has to work and toil a lot before one realizes these facts.''Comment: Published in at http://dx.doi.org/10.1214/088342306000000565 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Analysis of Mixed Outcomes: Misclassified Binary Responses and Measurement Error in Covariates

The focus of this paper is on regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are subject to classification errors. Latent variable is used to model the binary response. The joint distribution is expressed as a product of the marginal distribution of the continuous response and the conditional distribution of the binary response given the continuous response. Models are proposed to incorporate the measurement error and/or classification errors. Likelihood based analysis is performed to estimate the regression parameters of interest. Theoretical studies are made to find the bias of the likelihood estimates of the model parameters. An extensive simulation study is carried out to investigate the effect of ignoring classification errors and/or measurement error on the estimates of the model parameters. The methodology is illustrated with a data set obtained by conducting a small scale survey.

Does the Gravity Model Explain India Direction of Trade? A Panel Data Approach

In this paper we apply the gravity model to the panel consisting of India’s yearly bilateral trade data with all its trading partners in the second half of the twentieth century. The main conclusions that emerge from our analyses are: (1) The core gravity model can explain around 43 per cent of the fluctuations in India’s direction of trade in the second half of the twentieth century (2) India’s trade responds less than proportionally to size and more than proportionally to distance (3) Colonial heritage is still an important factor in determining India’s direction of trade at least in the second half of the twentieth century (4) India trades more with developed rather than underdeveloped countries, however (5) size has more determining influence on India’s trade than the level of development of the trading partner.

A Conceptual Overview of Structural Equation Modeling

A synthesized version of Structural Equation Modelling (SEM) and its possible applications in Management problems is presented. The main contribution of the paper is its simple description of a somewhat complex statistical process for the understanding of the beginners in this domain. It acts as a initial reading in SEM, before the researchers delve into more complex exposition of the statistical technique. The description is largely in English (not statistics) and is palatable to readers not trained enough in the domain of statistics. It will serve as a good overview of this methodology for FPM students in business schools.

Alleviating the inconsistencies in modelling decay of fissile compound nuclei

This work attempts to overcome the existing inconsistencies in modelling decay of fissile nucleus by inclusion of important physical effects in the model and through a systematic analysis of a large set of data over a wide range of CN mass (ACN). The model includes shell effect in the level density (LD) parameter, shell correction in the fission barrier, effect of the orientation degree of freedom of the CN spin (Kor), collective enhancement of level density (CELD) and dissipation in fission. Input parameters are not tuned to reproduce observables from specific reaction(s) and the reduced dissipation coefficient is treated as the only adjustable parameter. Calculated evaporation residue (ER) cross sections, fission cross sections and particle, i.e. neutron, proton and alpha-particle, multiplicities are compared with data covering ACN = 156-248. The model produces reasonable fits to ER and fission excitation functions for all the reactions considered in this work. Pre-scission neutron multiplicities are underestimated by the calculation beyond ACN~200. An increasingly higher value of pre-saddle dissipation strength is required to reproduce the data with increasing ACN. Proton and alpha-particle multiplicities, measured in coincidence with both ERs and fission fragments, are in qualitative agreement with model predictions. The present work mitigates the existing inconsistencies in modelling statistical decay of the fissile CN to a large extent.Comment: 15 pages, 9 figure

Quest for consistent modelling of statistical decay of the compound nucleus

A statistical model description of heavy ion induced fusion-fission reactions is presented where shell effects, collective enhancement of level density, tilting away effect of compound nuclear spin and dissipation are included. It is shown that the inclusion of all these effects provides a consistent picture of fission where fission hindrance is required to explain the experimental values of both pre-scission neutron multiplicities and evaporation residue cross-sections in contrast to some of the earlier works where a fission hindrance is required for pre-scission neutrons but a fission enhancement for evaporation residue cross-sections.Comment: 14 pages, 2 figure

Inference on Categorical Survey Response: A Predictive Approach

We consider the estimation of finite population proportions of categorical survey responses obtained by probability sampling. The customary design-based estimator does not make use of the auxiliary data available for all the population units at the estimation stage. We adopt a model-based predictive approach to incorporate this information and make the estimates more efficient. In the first part of our paper we consider a multinomial logit type model when logit function is a known parametric function of the covariates. We then use it for the prediction of non-sampled responses. This together with sampled responses is used to obtain the estimates of the proportions. The asymptotic biases and variances of these estimators are obtained. The main drawback of this approach is, being a parametric model it may suffer from model misspecification and thus, may lose it’s efficiencies over the usual design-based estimates. To overcome this drawback, in the next part of this paper we replace the multinomial logit type model by a nonparametric model using recently developed random coefficients splines models. Finally, we carry out a simulation study. It shows that the nonparametric approach may lead to an appreciable improvement over both parametric and design-based approaches when the regression function is quite different from multinomial logit.