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

Inference on the difference between two binomial proportions in the paired binomial setting is often an important problem in many biomedical investigations. Tang et al. (2010, Statistics in Medicine) discussed six methods to construct confidence intervals (henceforth, we abbreviate it as CI) for the difference between two proportions in paired binomial setting using method of variance estimates recovery. In this article, we propose weighted profile likelihood-based CIs for the difference between proportions of a paired binomial distribution. However, instead of the usual likelihood, we use weighted likelihood that is essentially making adjustments to the cell frequencies of a 2 × 2 table in the spirit of Agresti and Min (2005, Statistics in Medicine). We then conduct numerical studies to compare the performances of the proposed CIs with that of Tang et al. and Agresti and Min in terms of coverage probabilities and expected lengths. Our numerical study clearly indicates that the weighted profile likelihood-based intervals and Jeffreys interval (cf. Tang et al.) are superior in terms of achieving the nominal level, and in terms of expected lengths, they are competitive. Finally, we illustrate the use of the proposed CIs with real-life examples. Copyright © 2014 John Wiley & Sons, Ltd.

Weighted profile likelihood‐based confidence interval for the difference between two proportions with paired binomial data

Classification of pathological stage of prostate cancer patients using penalized splines

For the problem of testing absence of x.egression under the p-variate nonparametric linear regression set-up involving m predictors, standard rank test criteria are in the form of a quadratic form in mp linear rank statistics. Different standard tests correspond to different choices of one system of scores for each variable. In this paper we propose two test criteria which are based on simultaneous choice of more than one system of scores for each variable. The criteria are obtained by applying the union-intersection technique in two different ways. It turns out that for either criterion the use of several systems of scores for each variable results in an improvement in the asymptotic power.

Tathagata Banerjee

Papers

Weighted profile likelihood‐based confidence interval for the difference between two proportions with paired binomial data

Classification of pathological stage of prostate cancer patients using penalized splines

Combination of multiple scores for nonparametric testing in multivariate linear regression Set-Up

A new formulation of stress–strength reliability in a regression setup

Misclassification and Measurement Error Models in Epidemiologic Studies