This brief monograph is an in-depth study of the infinite divisibility and self-decomposability properties of central and noncentral Student's distributions, represented as variance and mean-variance mixtures of multivariate Gaussian distributions with the reciprocal gamma mixing distribution. These results allow us to define and analyse Student-Levy processes as Thorin subordinated Gaussian Levy processes. A broad class of one-dimensional, strictly stationary diffusions with the Student's t-marginal distribution are defined as the unique weak solution for the stochastic differential...
This brief monograph is an in-depth study of the infinite divisibility and self-decomposability properties of central and noncentral Student's distrib...
This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and...
This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly o...
This book presents recent advances (from 2008 to 2012) concerning use of the Naive Bayes model in unsupervised word sense disambiguation (WSD).
While WSD, in general, has a number of important applications in various fields of artificial intelligence (information retrieval, text processing, machine translation, message understanding, man-machine communication etc.), unsupervised WSD is considered important because it is language-independent and does not require previously annotated corpora. The Naive Bayes model has been widely used in supervised WSD, but its use in unsupervised WSD...
This book presents recent advances (from 2008 to 2012) concerning use of the Naive Bayes model in unsupervised word sense disambiguation (WSD).
In statistics, the Behrens-Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. In his 1935 paper, Fisher outlined an approach to the Behrens-Fisher problem. Since high-speed computers were not available in Fisher's time, this approach was not implementable and was soon forgotten. Fortunately, now that high-speed computers are available, this approach can easily...
In statistics, the Behrens-Fisher problem is the problem of interval estimation and hypothesis testing concerning the differenc...
This monograph is concerned with the fitting of linear relationships in the context of the linear statistical model. As alternatives to the familiar least squared residuals procedure, it investigates the relationships between the least absolute residuals, the minimax absolute residual and the least median of squared residuals procedures. It is intended for graduate students and research workers in statistics with some command of matrix analysis and linear programming techniques.
This monograph is concerned with the fitting of linear relationships in the context of the linear statistical model. As alternatives to the familiar l...
Methods for making inferences from data about one or more probabilities and proportions are a fundamental part of a statistician's toolbox and statistics courses. Unfortunately many of the quick, approximate methods currently taught have recently been found to be inappropriate. This monograph gives an up-to-date review of recent research on the topic and presents both exact methods and helpful approximations. Detailed theory is also presented for the different distributions involved, and can be used in a classroom setting. It will be useful for those teaching statistics at university...
Methods for making inferences from data about one or more probabilities and proportions are a fundamental part of a statistician's toolbox and ...
This book discusses important applications of the Behrens-Fisher statistic and the False Discovery Rate (FDR). Covered applications include ANOVA and MANOVA under potentially non-normal errors and heteroscedasticity; and an intuitive method of analyzing s x r contingency tables when the column variable is ordinal. This book also explores the novel possibility that these applications may be deemed nonparametric.
This book discusses important applications of the Behrens-Fisher statistic and the False Discovery Rate (FDR). Covered applications include ANOVA a...
This short book elaborates on selected aspects of stochastic-statistical dependencies in multivariate statistics. Each chapter provides a rigorous and self-contained treatment of one specific topic, poses a particular problem within its scope, and concludes by presenting its solution. The presented problems are not only relevant for research in mathematical statistics, but also entertaining, with elegant proofs and appealing solutions. The chapters cover correlation coefficients of bivariate normal distributions, empirical likelihood ratio tests for the population correlation, the...
This short book elaborates on selected aspects of stochastic-statistical dependencies in multivariate statistics. Each chapter provides a rigoro...
The purpose of this book is to illustrate a new statistical approach to test allelic association and genotype-specific effects in the genetic study of diseases. There are some parametric and non-parametric methods available for this purpose. We deal with population-based association studies, but comparisons with other methods will also be drawn, analysing the advantages and disadvantages of each one, particularly with regard to power properties with small sample sizes. In this framework we will work out some nonparametric statistical permutation tests and likelihood-based tests to perform...
The purpose of this book is to illustrate a new statistical approach to test allelic association and genotype-specific effects in the genetic study...
This book reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems of outliers.
This book reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic...
