PubMed · 3079512
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B Benhabyles. 1986. [Sampling surveys].. https://pubmed.ncbi.nlm.nih.gov/3079512/
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Because the goal of discriminant analysis (DA) is to optimise classification, it designedly exaggerates between-group differences. This bias complicates validation of DA. Jack-knifing has been used for validation but is inappropriate when stepwise selection (SWDA) is employed. A simple randomisation test is presented which is shown to give correct decisions for SWDA. The general superiority of randomisation tests over orthodox significance tests is discussed. Current work on non-parametric methods of estimating the error rates of prediction rules is briefly reviewed.
Classical repeated measures designs assume treatments are given in a randomized order. When randomization is not performed and an experiment involves a sequence of observations on each subject collected over time, serial correlations may become important. An example of these types of data is an intervention experiment wherein subjects are observed before and after a treatment or other manipulation. This situation falls within the realm of time series analysis. The correlations between observations often depend on the time intervals between the observations; observations that are closely spaced in time usually are more highly correlated than those with a larger time separation. This report demonstrates a test for such serial correlation and discusses a method of adjusting for it in repeated measures experiments.
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