Summary. A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y, as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9876.2005.00510.x

Generalized additive models for location, scale and shape

DAMBE (data analysis in molecular biology and evolution) is an integrated software package for converting, manipulating, statistically and graphically describing, and analyzing molecular sequence data with a user-friendly Windows 95/98/2000/NT interface. DAMBE is free and can be downloaded from http://web.hku.hk/~xxia/software/software.htm. The current version is 4.0.36.

/pdf/dambe-software-package-for-data-analysis-in-molecular-3rmgso6ejz.pdf

DAMBE: Software Package for Data Analysis in Molecular Biology and Evolution

This introductory book on the most useful parts of large-sample theory is designed to be accessible to scientists outside statistics and certainly to master’s-level statistics students who ignore most of measure theory. According to the author, “the subject of this book, first-order large- sample theory, constitutes a coherent body of concepts and results that are central to both theoretical and applied statistics.” All of the other existing books published on the subject over the last 20 years, from Ibragimov and Has’minskii in 1979 to the most recent by Van der Waart in 1998 have a common prerequisite in mathematical sophistication (measure theory in particular) that do not make the concepts available to a wide audience.

/pdf/elements-of-large-sample-theory-2ij6yyaggv.pdf

Elements of large-sample theory

Matching estimators are widely used in empirical economics for the evaluation of programs or treatments. Researchers using matching methods often apply the bootstrap to calculate the standard errors. However, no formal justification has been provided for the use of the bootstrap in this setting. In this article, we show that the standard bootstrap is, in general, not valid for matching estimators, even in the simple case with a single continuous covariate where the estimator is root-N consistent and asymptotically normally distributed with zero asymptotic bias. Valid inferential methods in this setting are the analytic asymptotic variance estimator of Abadie and Imbens (2006a) as well as certain modifications of the standard bootstrap, like the subsampling methods in Politis and Romano (1994).

/pdf/on-the-failure-of-the-bootstrap-for-matching-estimators-2osuyr1m3a.pdf

On the Failure of the Bootstrap for Matching Estimators

MicroRNAs have essential functional roles in brain development and neuronal specification but their roles in neurode- generative diseases such as Alzheimer's disease (AD) is unknown. Using a sensitive qRT-PCR platform we identified regional and stage-specific deregulation of miRNA expression in AD patient brains. We used experimental validation in addition to literature to reveal how the deregulated brain microRNAs are biomarkers for known and novel pathways in AD pathogenesis related to amyloid processing, neurogenesis, insulin resistance, and innate immunity. We additionally recovered miRNAs from cerebrospinal fluid and discovered AD-specific miRNA changes consistent with their role as potential biomarkers of disease.

S. Kocherlakota

Papers

Univariate Discrete Distributions.

Continuous Univariate Distributions.Vol. 1@@@Continuous Univariate Distributions.Vol. 2