Applied Nonparametric Statistical Methods
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Applied Nonparametric Statistical Methods

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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.show more

Product details

  • Hardback | 542 pages
  • 160 x 233.7 x 33mm | 884.52g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 4th Revised edition
  • 35 black & white illustrations
  • 158488701X
  • 9781584887010
  • 931,807

Review quote

... The greatest strength of this book is that it is written at a level that is perfectly understandable by readers with only a course or two of introductory-level statistics. As such, it is appropriate for use as either a textbook for a first course in nonparametric methods for undergraduate statistics majors or as a reference for practitioners in other fields. It is also quite suitable as a supplementary statistics textbook for graduate students ... . Key concepts are taught using worked-out examples from a variety of fields. ... a worthwhile choice for either an introductory-level textbook or a self-study reference for nonspecialists. The writing is very accessible and not weighted down by any mathematics beyond the grasp of the intended audience. ... -Psychometrika, Vol. 75, No. 3, September 2010 ... this book has an effective organization and covers a wider scope of non-parametric methods than former editions. Therefore, I believe that this book can serve its intended audience. -Journal of the Royal Statistical Society, Series A, Vol. 173, Issue 1, January 2010 Most fourth editions look surprisingly similar to the third editions. Applied Nonparametric Statistical Methods is an exception. Sprent and Smeeton have taken an accessible and well-regarded work and expanded, reorganized, and improved on it. ... Sprent and Smeeton offer a strong connection with respect to the how and why of the techniques. ... The book's major strength is its prioritization of coverage. The authors take painstaking care to inculcate an understanding of the appropriate use of nonparametric methods, as well as an appreciation for their application over a wide range of fields. The examples are well chosen, and the variety should ensure that every reader finds at least some of the problems interesting. ... As a competitor to the texts by Conover (1999), Gibbons and Chakraborti (2004), Higgins (2004), and Wasserman (2006), Applied Nonparametric Statistical Methods more than holds its own. The combination of clear writing and comprehensive coverage make it an excellent introductory text. ... -Technometrics, Vol. 51, No. 2, May 2009 ...The chapters have been substantially reorganized, and new material is provided on methods related to factorial designs and time-to-event data. An entirely new chapter, 'Modern Nonparametrics,' closes the text with a variety of topics ... the worked examples are thoroughly and meticulously done ... constant mention is made of the available software (e.g., StatXact, R, Minitab, SPSS) to conduct specific procedures. ... solutions to selected end-of-chapter exercises are annotated and quite helpful. Overall, this is a solid choice for a first course in nonparametric statistics for undergraduates. -Journal of the American Statistical Association, Vol. 104, No. 487, September 2009 ... expands coverage on the analysis of survival data and the bootstrap method. ... the new edition also focuses on some modern developments. The formal testing procedures are illustrated in a nice way with realistic examples leading to final conclusions, comments, and a discussion... The book has a clear style with well-organized material. The book works well as a reference book for users of nonparametric methods in different research areas. It is also a good textbook for undergraduate courses in statistics as well as courses for students majoring in other disciplines. -Hannu Oja, International Statistical Review, Vol. 27, No. 1, 2008 Praise for the Third Edition Strengths of this text certainly include its organization and writing style. Applied Nonparametric Statistical Methods provides a very clear exposition of modern nonparametric methods. Many students and practitioners will find it an excellent resource and reference for nonparametric statistics. -Technometrics, 2003 ... extremely valuable for statisticians as well as for researchers in applied fields. ... This well-written book is highly recommended for those readers who want to get a feeling for the nonparametric methods which they apply when analysing their data. -Statistics in Medicine, 2004show more

Table of contents

PREFACE SOME BASIC CONCEPTS Basic Statistics Populations and Samples Hypothesis Testing Estimation Ethical Issues FUNDAMENTALS OF NONPARAMETRIC METHODS A Permutation Test Binomial Tests Order Statistics and Ranks Exploring Data Efficiency of Nonparametric Procedures Computers and Nonparametric Methods Further Reading LOCATION INFERENCE FOR SINGLE SAMPLES Layout of Examples Continuous Data Samples Inferences about Medians Based on Ranks The Sign Test Use of Alternative Scores Comparing Tests and Robustness Fields of Application Summary OTHER SINGLE-SAMPLE INFERENCES Other Data Characteristics Matching Samples to Distributions Inferences for Dichotomous Data Tests Related to the Sign Test A Runs Test for Randomness Angular Data Fields of Application Summary METHODS FOR PAIRED SAMPLES Comparisons in Pairs A Less Obvious Use of the Sign Test Power and Sample Size Fields of Application Summary METHODS FOR TWO INDEPENDENT SAMPLES Centrality Tests and Estimates The Median Test Normal Scores Tests for Equality of Variance Tests for a Common Distribution Power and Sample Size Fields of Application Summary BASIC TESTS FOR THREE OR MORE SAMPLES Comparisons with Parametric Methods Centrality Tests for Independent Samples The Friedman Quade and Page Tests Binary Responses Tests for Heterogeneity of Variance Some Miscellaneous Considerations Fields of Application Summary ANALYSIS OF STRUCTURED DATA Factorial Treatment Structures Balanced 2 x 2 Factorial Structures The Nature of Interactions Alternative Approaches to Interactions Cross-Over Experiments Specific and Multiple Comparison Tests Fields of Application Summary Exercises ANALYSIS OF SURVIVAL DATA Special Features of Survival Data Modified Wilcoxon Tests Savage Scores and the Log-Rank Transformation Median Tests for Sequential Data Choice of Tests Fields of Application Summary CORRELATION AND CONCORDANCE Correlation in Bivariate Data Ranked Data for Several Variables Agreement Fields of Application Summary BIVARIATE LINEAR REGRESSION Fitting Straight Lines Fields of Application Summary CATEGORICAL DATA Categories and Counts Nominal Attribute Categories Ordered Categorical Data Goodness-of-fit Tests for Discrete Data Extension of McNemar's Test Fields of application Summary ASSOCIATION IN CATEGORICAL DATA The Analysis of Association Some Models for Contingency Tables Combining and Partitioning of Tables A Legal Dilemma Power Fields of Application Summary ROBUST ESTIMATION When Assumptions Break Down Outliers and Influence The Bootstrap M-estimators and Other Robust Estimators Fields of Application Summary MODERN NONPARAMETRICS A Change in Emphasis Density Estimation Regression Logistic Regression Multivariate Data New Methods for Large Data Sets Correlations within Clusters Summary Exercises appear in each chapter. APPENDIX 1 APPENDIX 2 REFERENCES INDEXshow more

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