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

https://jech.bmj.com/content/jech/47/6/511.4.full.pdf

Methodology for Genetic Studies of Twins and Families

The Analysis of Variance.

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.

https://iopscience.iop.org/article/10.1086/519947/pdf

Some Aspects of Measurement Error in Linear Regression of Astronomical Data

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.

Experiments: Planning, Analysis, and Parameter Design Optimization

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/.

/pdf/moabs-model-based-analysis-of-bisulfite-sequencing-data-398hoqmwjc.pdf

MOABS: model based analysis of bisulfite sequencing data


 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.

http://academic.oup.com/jssam/advance-article-pdf/doi/10.1093/jssam/smaa027/35118213/smaa027.pdf

Prediction of Finite Population Proportion When Responses Are Misclassified

The problem of testing equality of means of a bivariate normal distribution on the basis of a sample of size n has been considered when the labels of the observations are either missing or not known. The problem may arise in many applied settings, especially in genetics. Classical likelihood ratio test fails here because of identifiability problems. We propose a two-stage testing procedure using a recently developed test in the context of penalized splines. The proposed testing procedure is found to outperform the tests proposed in the literature. Copyright © 2017 John Wiley & Sons, Ltd.

Two‐stage test of means of unordered pairs

Over the past few decades, a substantial number of books have been published on the topic of survival analysis. This book is aimed at advanced graduate students and statistical researchers in statistics/biostatistics departments. It resembles the texts by Fleming and Harrington (1991) and Andersen, Borgan, Gill, and Keiding (1993), in the sense that it is based heavily on the counting process principles and associated semiparametric methods. The inspiration and influence of Andersen et al. (1993) on the presentation style, terminology, and approach to the subject are very visible in many parts of the book. The book’s main distinguishing feature is the authors’ deliberate attempt to go beyond the popular proportional hazards Cox model as the benchmark model for survival data. Here additive hazards model are introduced to the reader before the Cox model is introduced. The definition of partial likelihood in Chapter 3.2 is different from Cox’s partial likelihood. There are chapters devoted to multiplicative-additive models and transformation models. True to its title, the book has an extensive treatment of dynamic models and time-varying covariate effects. The strength of the book is in its presentation of the theories in an organized and very effective manner. The book also includes a nice discussion of longitudinal data from the standpoint of marked point process theory. The reader will definitely find choices about how much depth of the background theory he or she will like to delve into. Most will find the book much easier to read than generic texts considering a similar level of counting process and martingale theory in survival analysis. The book contains almost no discussion of parametric models for survival data. The discussion of frailty models and modeling aspects of multivariate and recurrent events data are very brief. The emphasis is clearly on semiparametric and marginal models, and the methods are restricted strictly to frequentist methods, based mainly on martingale-based asymptotic justifications. The authors clearly have not attempted to develop a comprehensive text on survival analysis models and methods. It will be difficult for this book to replace some of the popular textbooks for a graduate-level survival analysis course. However, it will be excellent as a reference book and possibly as a text for a second advanced-level course in survival analysis. The exercises at the end of each chapter are excellent. Every institution with a biostatistics/statistics graduate program should own a copy of this book in its library. The book supplies an extensive collection of R programs, including a manual for the “timereg” R package (in which the standard statistical methods used in the book are incorporated). The use of R programs for the implementation of the methods is a distinct and welcome feature. These R programs will be a good resource for a practitioner interested primarily in implementating the methods for practical data analysis. However, the potential users of this book (for data analysis) need to be sufficiently sophisticated in the jargon and interpretation of counting process principles of survival data analysis to appreciate the methods and explain the results to the clinicians and basic scientists. In summary, this book definitely deserves a place in the collection of any serious survival analyst. It is also recommended to theoretically sound data analysts interested in dynamic and semiparametric survival models beyond the class of multiplicative models.

Introductory Statistical Inference

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.''

Tathagata Banerjee

Papers

Prediction of Finite Population Proportion When Responses Are Misclassified

Two‐stage test of means of unordered pairs

Introductory Statistical Inference

A Conversation with Shoutir Kishore Chatterjee