Introduction To Non Parametric Methods Through R Software
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Introduction To Non Parametric Methods Through R Software
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Author : Editor IJSMI
language : en
Publisher: International Journal of Statistics and Medical Informatics
Release Date : 2022-09-30
Introduction To Non Parametric Methods Through R Software written by Editor IJSMI and has been published by International Journal of Statistics and Medical Informatics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Education categories.
Statistical Methods are widely used in Medical, Biological, Clinical, Business and Engineering field. The data which form the basis for the statistical methods helps us to take scientific and informed decisions. Statistical methods deal with the collection, compilation, analysis and making inference from the data. The book mainly focuses on non-parametric aspects of Statistical methods. Non parametric methods or tests are used when the assumption about the distribution of the variables in the data set is not known or does not follow normal distribution assumption. Non parametric methods are useful to deal with ordered categorical data. When the sample size is large, statistical tests are robust due to the central limit theorem property. When sample size is small one need to use non-parametric tests. Compared to parametric tests, non-parametric tests are less powerful i.e. if we fail to reject the null hypothesis even if it is false. When the data set involves ranks or measured in ordinal scale then non-parametric tests are useful and easy to construct than parametric tests. The book uses open source R statistical software to carry out different non-parametric statistical methods with sample datasets.
An Introduction To Nonparametric Statistics
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Author : John E. Kolassa
language : en
Publisher: CRC Press
Release Date : 2020-09-28
An Introduction To Nonparametric Statistics written by John E. Kolassa and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-28 with Mathematics categories.
An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Features Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented Tests are inverted to produce estimates and confidence intervals Multivariate tests are explored Techniques reflecting the dependence of a response variable on explanatory variables are presented Density estimation is explored The bootstrap and jackknife are discussed This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.
Nonparametric Statistics With Applications To Science And Engineering With R
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Author : Paul Kvam
language : en
Publisher: John Wiley & Sons
Release Date : 2022-10-18
Nonparametric Statistics With Applications To Science And Engineering With R written by Paul Kvam and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-18 with Mathematics categories.
NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.
Nonparametric Statistical Inference
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Author : Jean Dickinson Gibbons
language : en
Publisher: CRC Press
Release Date : 2020-12-21
Nonparametric Statistical Inference written by Jean Dickinson Gibbons and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-21 with Mathematics categories.
Praise for previous editions: "... a classic with a long history." – Statistical Papers "The fact that the first edition of this book was published in 1971 ... [is] testimony to the book’s success over a long period." – ISI Short Book Reviews "... one of the best books available for a theory course on nonparametric statistics. ... very well written and organized ... recommended for teachers and graduate students." – Biometrics "... There is no competitor for this book and its comprehensive development and application of nonparametric methods. Users of one of the earlier editions should certainly consider upgrading to this new edition." – Technometrics "... Useful to students and research workers ... a good textbook for a beginning graduate-level course in nonparametric statistics." – Journal of the American Statistical Association Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametrics. The Sixth Edition carries on this tradition and incorporates computer solutions based on R. Features Covers the most commonly used nonparametric procedures States the assumptions, develops the theory behind the procedures, and illustrates the techniques using realistic examples from the social, behavioral, and life sciences Presents tests of hypotheses, confidence-interval estimation, sample size determination, power, and comparisons of competing procedures Includes an Appendix of user-friendly tables needed for solutions to all data-oriented examples Gives examples of computer applications based on R, MINITAB, STATXACT, and SAS Lists over 100 new references Nonparametric Statistical Inference, Sixth Edition, has been thoroughly revised and rewritten to make it more readable and reader-friendly. All of the R solutions are new and make this book much more useful for applications in modern times. It has been updated throughout and contains 100 new citations, including some of the most recent, to make it more current and useful for researchers.
Applied Nonparametric Statistical Methods Fourth Edition
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Author : Peter Sprent
language : en
Publisher: CRC Press
Release Date : 2007-03-06
Applied Nonparametric Statistical Methods Fourth Edition written by Peter Sprent and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-06 with Mathematics categories.
While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments, such as large sample methods and computer-intensive applications. Keeping mathematics to a minimum, this text introduces nonparametric methods to undergraduate students who are taking either mainstream statistics courses or statistics courses within other disciplines. By giving the proper attention to data collection and the interpretation of analyses, it provides a full introduction to nonparametric methods.
An Introduction To Undergraduate Research In Computational And Mathematical Biology
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Author : Hannah Callender Highlander
language : en
Publisher: Springer Nature
Release Date : 2020-02-17
An Introduction To Undergraduate Research In Computational And Mathematical Biology written by Hannah Callender Highlander and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-17 with Mathematics categories.
Speaking directly to the growing importance of research experience in undergraduate mathematics programs, this volume offers suggestions for undergraduate-appropriate research projects in mathematical and computational biology for students and their faculty mentors. The aim of each chapter is twofold: for faculty, to alleviate the challenges of identifying accessible topics and advising students through the research process; for students, to provide sufficient background, additional references, and context to excite students in these areas and to enable them to successfully undertake these problems in their research. Some of the topics discussed include: • Oscillatory behaviors present in real-world applications, from seasonal outbreaks of childhood diseases to action potentials in neurons • Simulating bacterial growth, competition, and resistance with agent-based models and laboratory experiments • Network structure and the dynamics of biological systems • Using neural networks to identify bird species from birdsong samples • Modeling fluid flow induced by the motion of pulmonary cilia Aimed at undergraduate mathematics faculty and advanced undergraduate students, this unique guide will be a valuable resource for generating fruitful research collaborations between students and faculty.
Statistical Methods For Survival Data Analysis
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Author : Elisa T. Lee
language : en
Publisher: John Wiley & Sons
Release Date : 2013-09-23
Statistical Methods For Survival Data Analysis written by Elisa T. Lee and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-23 with Mathematics categories.
Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences. Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes: Marginal and random effect models for analyzing correlated censored or uncensored data Multiple types of two-sample and K-sample comparison analysis Updated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of the presented material Statistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
Core Concepts In Statistical Learning
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Author : Tushar Gulati
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Core Concepts In Statistical Learning written by Tushar Gulati and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
"Core Concepts in Statistical Learning" serves as a comprehensive introduction to fundamental techniques and concepts in statistical learning, tailored specifically for undergraduates in the United States. This book covers a broad range of topics essential for students looking to understand the intersection of statistics, data science, and machine learning. The book explores major topics, including supervised and unsupervised learning, model selection, and the latest algorithms in predictive analytics. Each chapter delves into methods like decision trees, neural networks, and support vector machines, ensuring readers grasp theoretical concepts and apply them to practical data analysis problems. Designed to be student-friendly, the text incorporates numerous examples, graphical illustrations, and real-world data sets to facilitate a deeper understanding of the material. Structured to support both classroom learning and self-study, it is a versatile resource for students across disciplines such as economics, biology, engineering, and more. Whether you're an aspiring data scientist or looking to enhance your analytical skills, "Core Concepts in Statistical Learning" provides the tools needed to navigate the complex landscape of modern data analysis and predictive modeling.
Introduction To Research Methodology For Specialists And Trainees
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Author : P. M. Shaughn O'Brien
language : en
Publisher: Cambridge University Press
Release Date : 2017-08-17
Introduction To Research Methodology For Specialists And Trainees written by P. M. Shaughn O'Brien and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-17 with Medical categories.
An updated, centralised volume for health-centred researchers, reflecting modern research from changing grant awarding expectations to software.
Advances In Computational Intelligence
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Author : Sudip Kumar Sahana
language : en
Publisher: Springer
Release Date : 2016-11-08
Advances In Computational Intelligence written by Sudip Kumar Sahana and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-08 with Technology & Engineering categories.
This volume comprises the proceedings of the International Conference on Computational Intelligence 2015 (ICCI15). This book aims to bring together work from leading academicians, scientists, researchers and research scholars from across the globe on all aspects of computational intelligence. The work is composed mainly of original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of computational intelligence. Specifically, the major topics covered include classical computational intelligence models and artificial intelligence, neural networks and deep learning, evolutionary swarm and particle algorithms, hybrid systems optimization, constraint programming, human-machine interaction, computational intelligence for the web analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, cross disciplinary topics and applications. The contents of this volume will be of use to researchers and professionals alike.