An Empirical Nonlinear Data-Fitting Approach for Transforming Data to Normality

A general method is proposed by which nonnormally distributed data can be transformed to achieve approximate normality. The method uses an empirical nonlinear data-fitting approach and can be applied to a broad class of transformations including the Box-Cox, arcsine, generalized logit, and Weibull-type transformations. It is easy to implement using standard statistical software packages. Several examples are provided.     

Comparing tests judged asymptotically equally efficient

For comparing two competing testing procedures whose power functions cannot be calculated exactly, the Pitman asymptotic relative efficiency is commonly used. This measure offers the advantages of simplicity in computation and interpretation, but is asymptotic in nature. In particular, it cannot distinguish between certain pairs of tests, namely, those with an asymptotic relative efficiency of 1. In this paper, we offer an alternative basis for making finite sample comparisons between tests judged asymptotically equally efficient. The criteria for comparison is based on the ratio of efficacies. Using this new criterion, we consider a series of examples in which two tests with an ARE of 1 are compared for finite n. In each case, either exact or simulated power curves are used to evaluate the effectiveness of this criterion in properly discriminating between the competing test procedures. Lastly, conditions are specified where the proposed criterion should be most effective.     

Modified cramer-von mises and anderson-darling tests for weibull distributions with unknown location and scale parameters

The standard Cramer-von Mises and Anderson-Darling goodness-of-fit tests require continuous underlying distributions with known parameters. In this paper, tables of critical values are generated for both tests for Weibull distributions with unknown location and scale parameters and known shape parameters. The powers of the Cramer-von Mises, Anderson-Darling, Kolmogorov-Smirnov, and Chi-Square tests for this situation are investigated. The Cramer-von Mises test has most power when the shape is 1.0 and the Anderson-Darling test has most power when the shape is 3.5. Finally, a relation between critical value and inverse shape parameter is presented.     

Bayesian estimation of proportions with a cross-entropy prior

This paper suggests estimators of the frequencies (N₈) or proportions {N₈/N) of N distinguishable objects contained in S categories; given various types of information, We consider information in the form of exact constraints on the N₈, sample frequencies, and frequencies of related data, The analysis uses Bayesian methods, where the prior distribution is assumed to be a function of the cross-entropy between the N₈ and a reference distribution, We show the relationship between our estimator and the log-linear and logit models and also present a sampling experiment to compare our proposed estimator with the iterated proportional fitting estimator.     

Evaluating Marginal Policy Changes and the Average Effect of Treatment for Individuals at the Margin

This paper develops methods for evaluating marginal policy changes. We characterize how the effects of marginal policy changes depend on the direction of the policy change, and show that marginal policy effects are fundamentally easier to identify and to estimate than conventional treatment parameters. We develop the connection between marginal policy effects and the average effect of treatment for persons on the margin of indifference between participation in treatment and nonparticipation, and use this connection to analyze both parameters. We apply our analysis to estimate the effect of marginal changes in tuition on the return to going to college.     

EFFECTS OF THE PARENTAL COALITION ON ADOLESCENT EMANCIPATION FROM THE FAMILY

This study examined the relationship between late adolescents' perception of their parents' marital coalition and academic success as a college freshman. The hypothesis was tested that late adolescents from families with a primary cross-generational bond (e.g., mother-son) have more difficulty emancipating from the family than offspring from families in which mother and father are the primary dyadic alliance in the family. Academic success as a college freshman was utilized as one measure of the late adolescents' success in separating from the family. The subjects were 36 18–year-old freshman males placed on academic probation and 36 passing males matched on ethnicity and SAT scores. The principal results were that subjects reporting a primary marital alliance were more likely to succeed academically and were more internal on the Rotter I-E scale than subjects who reported a nonmarital alliance as primary. The findings provide support for the theory of structural family relations, and clarify familial processes that affect late adolescent emancipation from the family.