Nonparametric Methods in Statistics with SAS Applications

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A01=Olga Korosteleva
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Ansari Bradley Test
apply nonparametric techniques to statistical data
Author_Olga Korosteleva
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binary logistic and Poisson models
Bivariate Spline
Bootstrap Estimation Method
Bootstrap Samples
Category1=Non-Fiction
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Category=PBT
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CORR Procedure
Cumulative Distribution Function
Data Set
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density estimation
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Friedman Rank Test
graduate course in applied statistics
HS Grad
Kaplan Meier Estimator
Km Estimator
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loess and thin-plate splines
Loess Regression
nonparametric hypotheses testing
nonparametric techniques for survival analysis
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Proc Corr Data
Proc Freq Data
Proc G3
Proc Print Data
Proc Sort Data
Proc Univariate Data
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regression modeling
resampling methods
SAS Applications of Nonparametric Methods
SAS Implementation
SAS Output
Smoothing Parameter
smoothing techniques for nonparametric regression
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Spearman Correlation
Spearman Rank Correlation Coefficient
time-to-event analysis
Triangular Kernel
Wilcoxon Rank Sum Test

Product details

  • ISBN 9781466580626
  • Weight: 360g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Aug 2013
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
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Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods.

The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation.

Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

Olga Korosteleva is an associate professor of statistics in the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received a Ph.D. in statistics from Purdue University.