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Books in Statistics

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21-30 of 145 results in All results

Reliability Modelling and Analysis in Discrete Time

  • 1st Edition
  • May 15, 2018
  • Unnikrishnan Nair + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 1 9 1 3 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 0 0 6 - 7
Reliability Modelling and Analysis in Discrete Time provides an overview of the probabilistic and statistical aspects connected with discrete reliability systems. This engaging book discusses their distributional properties and dependence structures before exploring various orderings associated between different reliability structures. Though clear explanations, multiple examples, and exhaustive coverage of the basic and advanced topics of research in this area, the work gives the reader a thorough understanding of the theory and concepts associated with discrete models and reliability structures. A comprehensive bibliography assists readers who are interested in further research and understanding. Requiring only an introductory understanding of statistics, this book offers valuable insight and coverage for students and researchers in Probability and Statistics, Electrical Engineering, and Reliability/Quality Engineering. The book also includes a comprehensive bibliography to assist readers seeking to delve deeper.

Statistical Inference in Financial and Insurance Mathematics with R

  • 1st Edition
  • November 22, 2017
  • Alexandre Brouste
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 0 8 3 - 6
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 2 6 1 - 1
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.

Handbook of Statistical Analysis and Data Mining Applications

  • 2nd Edition
  • November 9, 2017
  • Ken Yale + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 4 1 6 6 3 2 - 5
  • eBook
    9 7 8 - 0 - 1 2 - 4 1 6 6 4 5 - 5
Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce.

Disease Modelling and Public Health, Part B

  • 1st Edition
  • Volume 37
  • October 31, 2017
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 3 9 7 5 - 2
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 3 9 7 6 - 9
Handbook of Statistics: Disease Modelling and Public Health, Part B, Volume 37 addresses new challenges in existing and emerging diseases. As a two part volume, this title covers an extensive range of techniques in the field, with this book including chapters on Reaction diffusion equations and their application on bacterial communication, Spike and slab methods in disease modeling, Mathematical modeling of mass screening and parameter estimation, Individual-based and agent-based models for infectious disease transmission and evolution: an overview, and a section on Visual Clustering of Static and Dynamic High Dimensional Data. This volume covers the lack of availability of complete data relating to disease symptoms and disease epidemiology, one of the biggest challenges facing vaccine developers, public health planners, epidemiologists and health sector researchers.

Disease Modelling and Public Health, Part A

  • 1st Edition
  • Volume 36
  • October 13, 2017
  • Arni S.R. Srinivasa Rao + 2 more
  • English
  • Hardback
    9 7 8 - 0 - 4 4 4 - 6 3 9 6 8 - 4
  • eBook
    9 7 8 - 0 - 4 4 4 - 6 3 9 6 9 - 1
Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology. As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.

Biostatistics and Computer-based Analysis of Health Data Using SAS

  • 1st Edition
  • June 22, 2017
  • Christophe Lalanne + 1 more
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 1 1 1 - 6
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 1 7 1 - 3
This volume of the Biostatistics and Health Sciences Set focuses on statistics applied to clinical research.The use of SAS for data management and statistical modeling is illustrated using various examples.  Many aspects of data processing and statistical analysis of cross-sectional and experimental medical data are covered, including regression models commonly found in medical statistics. This practical book is primarily intended for health researchers with a basic knowledge of statistical methodology. Assuming basic concepts, the authors focus on the practice of biostatistical methods essential to clinical research, epidemiology and analysis of biomedical data (including comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis).  The use of examples from clinical trials and epidemiological studies provide the basis for a series of practical exercises, which provide instruction and familiarize the reader with essential SAS commands.

Optimal Sports Math, Statistics, and Fantasy

  • 1st Edition
  • April 6, 2017
  • Robert Kissell + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 5 1 6 3 - 4
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 5 2 9 3 - 8
Optimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required to objectively analyze sports teams, evaluate player performance, and predict game outcomes. These techniques can also be applied to fantasy sports competitions.   Readers will learn how to: Accurately rank sports teams Compute winning probability Calculate expected victory margin Determine the set of factors that are most predictive of team and player performance Optimal Sports Math, Statistics, and Fantasy also illustrates modeling techniques that can be used to decode and demystify the mysterious computer ranking schemes that are often employed by post-season tournament selection committees in college and professional sports. These methods offer readers a verifiable and unbiased approach to evaluate and rank teams, and the proper statistical procedures to test and evaluate the accuracy of different models.   Optimal Sports Math, Statistics, and Fantasy delivers a proven best-in-class quantitative modeling framework with numerous applications throughout the sports world.

Introductory Statistics

  • 4th Edition
  • January 26, 2017
  • Sheldon M. Ross
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 8 0 4 3 1 7 - 2
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 4 3 6 1 - 5
Introductory Statistics, Fourth Edition, reviews statistical concepts and techniques in a manner that will teach students not only how and when to utilize the statistical procedures developed, but also how to understand why these procedures should be used. The text's main merits are the clarity of presentation, contemporary examples and applications from diverse areas, an explanation of intuition, and the ideas behind the statistical methods. Concepts are motivated, illustrated, and explained in a way that attempts to increase one's intuition. To quote from the preface, it is only when a student develops a feel or intuition for statistics that she or he is really on the path toward making sense of data. Ross achieves this goal through a coherent mix of mathematical analysis, intuitive discussions, and examples. Applications and examples refer to real-world issues, such as gun control, stock price models, health issues, driving age limits, school admission ages, use of helmets, sports, scientific fraud, and many others. Examples relating to data mining techniques using the number of Google queries or Twitter tweets are also considered. For this fourth edition, new topical coverage includes sections on Pareto distribution and the 80-20 rule, Benford's law, added material on odds and joint distributions and correlation, logistic regression, A-B testing, and more modern (big data) examples and exercises.

Simulation of Stochastic Processes with Given Accuracy and Reliability

  • 1st Edition
  • November 22, 2016
  • Yuriy V. Kozachenko + 3 more
  • English
  • Hardback
    9 7 8 - 1 - 7 8 5 4 8 - 2 1 7 - 5
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 2 0 8 5 - 2
Simulation has now become an integral part of research and development across many fields of study. Despite the large amounts of literature in the field of simulation and modeling, one recurring problem is the issue of accuracy and confidence level of constructed models. By outlining the new approaches and modern methods of simulation of stochastic processes, this book provides methods and tools in measuring accuracy and reliability in functional spaces.The authors explore analysis of the theory of Sub-Gaussian (including Gaussian one) and Square Gaussian random variables and processes and Cox processes.  Methods of simulation of stochastic processes and fields with given accuracy and reliability in some Banach spaces are also considered.

Analysis for Time-to-Event Data under Censoring and Truncation

  • 1st Edition
  • September 26, 2016
  • Hongsheng Dai + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 5 4 8 0 - 2
  • eBook
    9 7 8 - 0 - 0 8 - 1 0 1 0 0 8 - 2
Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors.