mathematical statistics basic ideas and selected topics volume i second edition 1 chapman hall crc texts in statistical science

Download Book Mathematical Statistics Basic Ideas And Selected Topics Volume I Second Edition 1 Chapman Hall Crc Texts In Statistical Science in PDF format. You can Read Online Mathematical Statistics Basic Ideas And Selected Topics Volume I Second Edition 1 Chapman Hall Crc Texts In Statistical Science here in PDF, EPUB, Mobi or Docx formats.

Mathematical Statistics

Author : Peter J. Bickel
ISBN : 9781498723817
Genre : Business & Economics
File Size : 80. 82 MB
Format : PDF, Docs
Download : 543
Read : 895

Download Now


Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. The book first discusses non- and semiparametric models before covering parameters and parametric models. It then offers a detailed treatment of maximum likelihood estimates (MLEs) and examines the theory of testing and confidence regions, including optimality theory for estimation and elementary robustness considerations. It next presents basic asymptotic approximations with one-dimensional parameter models as examples. The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLEs, Wald and Rao statistics, generalized linear models, and more. Mathematical Statistics: Basic Ideas and Selected Topics, Volume II will be published in 2015. It will present important statistical concepts, methods, and tools not covered in Volume I.

Practical Statistics For Medical Research

Author : Douglas G. Altman
ISBN : 0412276305
Genre : Mathematics
File Size : 67. 91 MB
Format : PDF, ePub
Download : 817
Read : 1305

Download Now


Most medical researchers, whether clinical or non-clinical, receive some background in statistics as undergraduates. However, it is most often brief, a long time ago, and largely forgotten by the time it is needed. Furthermore, many introductory texts fall short of adequately explaining the underlying concepts of statistics, and often are divorced from the reality of conducting and assessing medical research. Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. The text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.

Books In Print

Author :
ISBN : UOM:39015062097707
Genre : American literature
File Size : 90. 59 MB
Format : PDF, ePub
Download : 912
Read : 1104

Download Now


Books in print is the major source of information on books currently published and in print in the United States. The database provides the record of forthcoming books, books in-print, and books out-of-print.

Applied Statistics Principles And Examples

Author : D.R. Cox
ISBN : 0412165708
Genre : Mathematics
File Size : 67. 15 MB
Format : PDF
Download : 911
Read : 883

Download Now


This book should be of interest to senior undergraduate and postgraduate students of applied statistics.

Linear Models With R

Author : Julian J. Faraway
ISBN : 9780203507278
Genre : Mathematics
File Size : 36. 23 MB
Format : PDF, ePub
Download : 855
Read : 1041

Download Now


Books on regression and the analysis of variance abound—many are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and, more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs. It also discusses topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates numerous examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://people.bath.ac.uk/jjf23/LMR/ The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it.

Journal Of The American Statistical Association

Author :
ISBN : UOM:39015085199381
Genre : Statistics
File Size : 31. 33 MB
Format : PDF
Download : 945
Read : 1314

Download Now



Extending The Linear Model With R

Author : Julian J. Faraway
ISBN : 0203492285
Genre : Mathematics
File Size : 65. 54 MB
Format : PDF, Mobi
Download : 584
Read : 563

Download Now


Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

Stochastic Modelling For Systems Biology Second Edition

Author : Darren J. Wilkinson
ISBN : 9781439837726
Genre : Mathematics
File Size : 74. 68 MB
Format : PDF, ePub, Docs
Download : 508
Read : 455

Download Now


Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Re-written to reflect this modern perspective, this second edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. Keeping with the spirit of the first edition, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. New in the Second Edition All examples have been updated to Systems Biology Markup Language Level 3 All code relating to simulation, analysis, and inference for stochastic kinetic models has been re-written and re-structured in a more modular way An ancillary website provides links, resources, errata, and up-to-date information on installation and use of the associated R package More background material on the theory of Markov processes and stochastic differential equations, providing more substance for mathematically inclined readers Discussion of some of the more advanced concepts relating to stochastic kinetic models, such as random time change representations, Kolmogorov equations, Fokker-Planck equations and the linear noise approximation Simple modelling of "extrinsic" and "intrinsic" noise An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional mathematical detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

Bayesian Data Analysis Third Edition

Author : Andrew Gelman
ISBN : 9781439840955
Genre : Mathematics
File Size : 23. 8 MB
Format : PDF, ePub, Mobi
Download : 364
Read : 1003

Download Now


Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Statistical Methods In Agriculture And Experimental Biology Third Edition

Author : Roger Mead
ISBN : 9781584881872
Genre : Mathematics
File Size : 51. 68 MB
Format : PDF, Kindle
Download : 441
Read : 295

Download Now


The third edition of this popular introductory text maintains the character that won worldwide respect for its predecessors but features a number of enhancements that broaden its scope, increase its utility, and bring the treatment thoroughly up to date. It provides complete coverage of the statistical ideas and methods essential to students in agriculture or experimental biology. In addition to covering fundamental methodology, this treatment also includes more advanced topics that the authors believe help develop an appreciation of the breadth of statistical methodology now available. The emphasis is not on mathematical detail, but on ensuring students understand why and when various methods should be used. New in the Third Edition: A chapter on the two simplest yet most important methods of multivariate analysis Increased emphasis on modern computer applications Discussions on a wider range of data types and the graphical display of data Analysis of mixed cropping experiments and on-farm experiments

Top Download:

Best Books