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Books in Statistics

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41-50 of 145 results in All results

Exact Statistical Inference for Categorical Data

  • 1st Edition
  • October 30, 2015
  • Guogen Shan
  • English
  • Paperback
    9 7 8 - 0 - 0 8 - 1 0 0 6 8 1 - 8
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 9 4 8 - 9
Exact Statistical Inference for Categorical Data discusses the way asymptotic approaches have been often used in practice to make statistical inference. This book introduces both conditional and unconditional exact approaches for the data in 2 by 2, or 2 by k contingency tables, and is an ideal reference for users who are interested in having the convenience of applying asymptotic approaches, with less computational time. In addition to the existing conditional exact inference, some efficient, unconditional exact approaches could be used in data analysis to improve the performance of the testing procedure.

Hidden Semi-Markov Models

  • 1st Edition
  • October 22, 2015
  • Shun-Zheng Yu
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 7 6 7 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 7 7 1 - 4
Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

The Birnbaum-Saunders Distribution

  • 1st Edition
  • October 22, 2015
  • Victor Leiva
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 3 7 6 9 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 8 2 7 - 7
The Birnbaum-Saunders Distribution presents the statistical theory, methodology, and applications of the Birnbaum-Saunders distribution, a very flexible distribution for modeling different types of data (mainly lifetime data). The book describes the most recent theoretical developments of this model, including properties, transformations and related distributions, lifetime analysis, and shape analysis. It discusses methods of inference based on uncensored and censored data, goodness-of-fit tests, and random number generation algorithms for the Birnbaum-Saunders distribution, also presenting existing and future applications.

Introduction to Statistical Machine Learning

  • 1st Edition
  • September 25, 2015
  • Masashi Sugiyama
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 1 2 1 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 3 5 0 - 1
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.

Biostatistics for Medical and Biomedical Practitioners

  • 1st Edition
  • September 3, 2015
  • Julien I. E. Hoffman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 3 8 7 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 6 0 7 - 6
Biostatistics for Practitioners: An Interpretative Guide for Medicine and Biology deals with several aspects of statistics that are indispensable for researchers and students across the biomedical sciences. The book features a step-by-step approach, focusing on standard statistical tests, as well as discussions of the most common errors. The book is based on the author’s 40+ years of teaching statistics to medical fellows and biomedical researchers across a wide range of fields.

Essential Statistics, Regression, and Econometrics

  • 2nd Edition
  • June 8, 2015
  • Gary Smith
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 3 4 5 9 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 3 4 9 2 - 7
Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphasis on statistical reasoning, real data, pitfalls in data analysis, and modeling issues. This book is uncommonly approachable and easy to use, with extensive word problems that emphasize intuition and understanding. Too many students mistakenly believe that statistics courses are too abstract, mathematical, and tedious to be useful or interesting. To demonstrate the power, elegance, and even beauty of statistical reasoning, this book provides hundreds of new and updated interesting and relevant examples, and discusses not only the uses but also the abuses of statistics. The examples are drawn from many areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life.

Semi-Markov Models

  • 1st Edition
  • February 2, 2015
  • Yuriy E Obzherin + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 2 1 2 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 4 8 6 - 7
Featuring previously unpublished results, Semi-Markov Models: Control of Restorable Systems with Latent Failures describes valuable methodology which can be used by readers to build mathematical models of a wide class of systems for various applications. In particular, this information can be applied to build models of reliability, queuing systems, and technical control. Beginning with a brief introduction to the area, the book covers semi-Markov models for different control strategies in one-component systems, defining their stationary characteristics of reliability and efficiency, and utilizing the method of asymptotic phase enlargement developed by V.S. Korolyuk and A.F. Turbin. The work then explores semi-Markov models of latent failures control in two-component systems. Building on these results, solutions are provided for the problems of optimal periodicity of control execution. Finally, the book presents a comparative analysis of analytical and imitational modeling of some one- and two-component systems, before discussing practical applications of the results

Computational Statistics with R

  • 1st Edition
  • Volume 32
  • November 25, 2014
  • Marepalli B. Rao + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 3 4 3 1 - 3
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 3 4 4 1 - 2
R is open source statistical computing software. Since the R core group was formed in 1997, R has been extended by a very large number of packages with extensive documentation along with examples freely available on the internet. It offers a large number of statistical and numerical methods and graphical tools and visualization of extraordinarily high quality. R was recently ranked in 14th place by the Transparent Language Popularity Index and 6th as a scripting language, after PHP, Python, and Perl. The book is designed so that it can be used right away by novices while appealing to experienced users as well. Each article begins with a data example that can be downloaded directly from the R website. Data analysis questions are articulated following the presentation of the data. The necessary R commands are spelled out and executed and the output is presented and discussed. Other examples of data sets with a different flavor and different set of commands but following the theme of the article are presented as well. Each chapter predents a hands-on-experience. R has superb graphical outlays and the book brings out the essentials in this arena. The end user can benefit immensely by applying the graphics to enhance research findings. The core statistical methodologies such as regression, survival analysis, and discrete data are all covered.

Doing Bayesian Data Analysis

  • 2nd Edition
  • November 3, 2014
  • John Kruschke
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 4 0 5 8 8 8 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 4 0 5 9 1 6 - 0
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.

An Introduction to Probability and Statistical Inference

  • 2nd Edition
  • September 25, 2014
  • George G. Roussas
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 0 1 1 4 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 0 4 3 7 - 1
An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture.