Nonparametric analysis of longitudinal data in factorial experiments

By Edgar Brunner, S. Domhof, F. Langer. 2012

1,407 citations recorded by the source corpus.

Preface. Acknowledgments. Introduction. Models. Effects and Hypotheses. Estimators for Relative Effects. Test Statistics. Software. Experiments for One Group of Subjects. Experiements for Several Groups of Subjects. Dependent Replications. Multifactorial Experiments. Numerous Time Points. References. Appendix A: Original Data. Appendix B: Results from the SAS Macros. Glossary. Index.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d24091706f953f84acc3edd844cfa8cfb3581276

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Modern robust data analysis methods: measures of central tendency. (2003, methodology): “For a summary of important and useful developments related to rank-based techniques, see Brunner, Domhof, and Langer (2002); Cliff (1996); and Wilcox (2003).”
Modern robust statistical methods: an easy way to maximize the accuracy and power of your research. (2008, background): “Brunner et al. (2002) developed macros that allow the easy generation of the ATS in SAS.”
Nonparametric Analysis of Longitudinal Data in Factorial Experiments (2003, methodology): “Moreover, such nonparametric methods are robust to outliers and exhibit competitive performance for small sample sizes (Brunner, Domhof, and Langer 2002, Section 1.1, pp. 2).”
Modeling and Measuring the Structure of Professional Vision in Preservice Teachers (2014, methodology): “Due the small sample size, we used the R-package nparLD for nonparametric variance analysis of longitudinal data that has been shown as robust method even for small sample size (Noguchi et al., 2012).”