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Neuhaus, Georg – Journal of Multivariate Analysis, 1976
The asymptotic power of the Cramer-von Mises test when parameters are estimated from the data is studied under certain local (contiguous) alternatives. Notion of (asymptotic) direction and distance from the null hypothesis of alternatives is introduced, and it is shown that there exist directions with maximum, minimum, and arbitrary intermediate…
Descriptors: Goodness of Fit, Hypothesis Testing, Nonparametric Statistics, Probability
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Wilcox, Rand R. – Educational and Psychological Measurement, 2006
Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…
Descriptors: Nonparametric Statistics, Mathematical Models, Regression (Statistics), Probability
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Meyer, Lennart – Educational and Psychological Measurement, 1979
The PM statistical index, which indicates the probability that a person will belong to a particular clinical class, is described. The coefficient is similar to the G index but is easier to compute. An empirical example is presented. (JKS)
Descriptors: Adults, Clinical Diagnosis, Data Analysis, Hypothesis Testing
Keats, John B.; Brewer, James K. – 1971
This paper presents an index of goodness-of-fit for comparing m models over n trials. The index allows for differentiated weighting of the trials as to their importance in the comparison of the models. Several possible weighting schemes are suggested and the conditions on the weights which assure asymptotic normality of the index distribution are…
Descriptors: Goodness of Fit, Hypothesis Testing, Mathematical Models, Nonparametric Statistics
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Edgington, Eugene S.; Haller, Otto – Educational and Psychological Measurement, 1984
This paper explains how to combine probabilities from discrete distributions, such as probability distributions for nonparametric tests. (Author/BW)
Descriptors: Computer Software, Data Analysis, Hypothesis Testing, Mathematical Formulas
Koffler, Stephen L. – 1976
The power of the classical Linear Discriminant Function (LDF) is compared, using Monte Carlo techniques with five other procedures for classifying observations from certain non-normal distributions. The alternative procedures considered are the Quadratic Discriminant Function, a Nearest Neighbor Procedure with Probability Blocks, and three density…
Descriptors: Behavioral Science Research, Classification, Comparative Analysis, Discriminant Analysis