## Nonparametric analysis of longitudinal data in factorial experimentsThe authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear models approach. Emphasizing the practical application of these methods in statistical procedures, this book provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data. Topics covered include nonparametric models, effects and hypotheses in experimental design, estimators for relative effects, experiments for one and several groups of subjects, multifactorial experiments, dependent replications, and experiments with numerous time points. The basic mathematical principles for the methods introduced here are described in theory, consistent with the book's minimal math requirements. Simple approximations for small data sets are provided, as well as ample chapter exercises to test skills, an appendix that includes original data for the examples used throughout the book, and downloadable SAS-IML macros for implementing the more extensive calculations. All applications are designed to be useful in many fields. Generously supplemented with more than 110 graphs and tables, Nonparametric Analysis of Longitudinal Data in Factorial Experiments is an essential reference for statisticians and biometricians, researchers in clinical trials, psychological studies, and in the fields of forestry, agriculture, sociology, ecology, and biology, as well as graduate students in statistics and biostatistics. |

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a-amylase study agoraphobia Akritas analysis ANOVA ANOVA-type statistics Applications approximation area*year asymptotically box plots Brunner centering matrix computed confidence intervals considered contrast matrix corresponding Cortisol covariance matrix curves degrees of freedom denotes dependent replications Dobutamine drug empirical distribution function experimental design Fenoterol Fn(T formulated group effect group of subjects homogeneous group Hq CF hypothesis Hq interaction Layout levels linear model longitudinal data macro LD_CI marginal distributions midranks missing values multivariate multivariate normal distribution nonparametric nonparametric models normal distribution number of observations obtains original data p-value panic disorder study parameters patterned alternatives placebo points probands profiles Propanolol random variables rank means relative effects pi relative marginal effects relative treatment effects repeated measures roof SAS macro SAS standard procedures Section shoulder tip pain structure study Example sub-plot factor summary variables Table tip pain study total number variable numeric variance whole-plot factor Wistar rats