Frontiers In Probability And The Statistical Sciences PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Frontiers In Probability And The Statistical Sciences PDF full book. Access full book title Frontiers In Probability And The Statistical Sciences.

Statistical Analysis of Next Generation Sequencing Data

Statistical Analysis of Next Generation Sequencing Data
Author: Somnath Datta
Publisher: Springer
Total Pages: 438
Release: 2014-07-03
Genre: Medical
ISBN: 3319072129

Download Statistical Analysis of Next Generation Sequencing Data Book in PDF, ePub and Kindle

Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.


Statistical Methods for Ranking Data

Statistical Methods for Ranking Data
Author: Mayer Alvo
Publisher: Springer
Total Pages: 276
Release: 2014-09-02
Genre: Mathematics
ISBN: 1493914715

Download Statistical Methods for Ranking Data Book in PDF, ePub and Kindle

This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.


Statistical Analysis of Microbiome Data

Statistical Analysis of Microbiome Data
Author: Somnath Datta
Publisher: Springer Nature
Total Pages: 349
Release: 2021-10-27
Genre: Medical
ISBN: 3030733513

Download Statistical Analysis of Microbiome Data Book in PDF, ePub and Kindle

Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.


Frontiers in Statistics

Frontiers in Statistics
Author: Jianqing Fan
Publisher: World Scientific
Total Pages: 552
Release: 2006
Genre: Mathematics
ISBN: 1860946704

Download Frontiers in Statistics Book in PDF, ePub and Kindle

During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics.Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets.This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel's distinguished contributions.


Multivariate Statistical Methods

Multivariate Statistical Methods
Author: György Terdik
Publisher: Springer Nature
Total Pages: 424
Release: 2021-10-26
Genre: Mathematics
ISBN: 3030813924

Download Multivariate Statistical Methods Book in PDF, ePub and Kindle

This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations. Multivariate nonlinear statistical models require the study of higher-order moments and cumulants. The main tool used for the definitions is the tensor derivative, leading to several useful expressions concerning Hermite polynomials, moments, cumulants, skewness, and kurtosis. A general test of multivariate skewness and kurtosis is obtained from this treatment. Exercises are provided for each chapter to help the readers understand the methods. Lastly, the book includes a comprehensive list of references, equipping readers to explore further on their own.


Artificial Intelligence Frontiers in Statistics

Artificial Intelligence Frontiers in Statistics
Author: David J. Hand
Publisher: CRC Press
Total Pages: 431
Release: 2020-11-26
Genre: Business & Economics
ISBN: 100015291X

Download Artificial Intelligence Frontiers in Statistics Book in PDF, ePub and Kindle

This book presents a summary of recent work on the interface between artificial intelligence and statistics. It does this through a series of papers by different authors working in different areas of this interface. These papers are a selected and referenced subset of papers presented at the 3rd Interntional Workshop on Artificial Intelligence and Statistics, Florida, January 1991.


Frontiers of Statistical Decision Making and Bayesian Analysis

Frontiers of Statistical Decision Making and Bayesian Analysis
Author: Ming-Hui Chen
Publisher: Springer Science & Business Media
Total Pages: 631
Release: 2010-07-24
Genre: Mathematics
ISBN: 1441969446

Download Frontiers of Statistical Decision Making and Bayesian Analysis Book in PDF, ePub and Kindle

Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.


Bayesian Statistics for Beginners

Bayesian Statistics for Beginners
Author: Therese M. Donovan
Publisher: Oxford University Press, USA
Total Pages: 430
Release: 2019
Genre: Mathematics
ISBN: 0198841299

Download Bayesian Statistics for Beginners Book in PDF, ePub and Kindle

This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.


Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
Author: Susmita Datta
Publisher: Springer
Total Pages: 294
Release: 2016-12-15
Genre: Medical
ISBN: 3319458094

Download Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry Book in PDF, ePub and Kindle

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.