Bounds and approximations for pairwise comparisons of independent multinormal means

Properties and relationships of some commonly used probability bounds, along with other recently developed bounds and approximations, are evaluated for their performance with pairwise comparisons. The comparisons are of independent sample means obtained from normal random variables with a common variance. Computational methods are presented and numerical results are used to further evaluate the performance of the bounds.     

Estimation of river and stream temperature trends under haphazard sampling

Long-term temporal trends in water temperature in rivers and streams are typically estimated under the assumption of evenly-spaced space-time measurements. However, sampling times and dates associated with historical water temperature datasets and some sampling designs may be haphazard. As a result, trends in temperature may be confounded with trends in time or space of sampling which, in turn, may yield biased trend estimators and thus unreliable conclusions. We address this concern using multilevel (hierarchical) linear models, where time effects are allowed to vary randomly by day and date effects by year. We evaluate the proposed approach by Monte Carlo simulations with imbalance, sparse data and confounding by trend in time and date of sampling. Simulation results indicate unbiased trend estimators while results from a case study of temperature data from the Illinois River, USA conform to river thermal assumptions. We also propose a new nonparametric bootstrap inference on multilevel models that allows for a relatively flexible and distribution-free quantification of uncertainties. The proposed multilevel modeling approach may be elaborated to accommodate nonlinearities within days and years when sampling times or dates typically span temperature extremes.     

Growth curve analysis of complete and incomplete longitudinal data

Growth curve analysis is reviewed for both complete and incomplete data structures. Specific attention is paid to the incomplete data model of Kleinbaum and its applicability to the mixed longitudinal study. Design considerations and efficiency for various incomplete longitudinal studies are also discussed.     

Residents,recreationists, ridge regression,and revenues:the five r's of florida's motor- tax

Ridge regression is applied to the question of how to revise Florida's motor-fuel tax to remedy the shortfall of its transportation revenues. Along the way a model of gasoline consumption by residents and tourists is presented, and the differences in consumption patterns between the two types of purchasers are reported.     

Performance of the weighted least squares apprdach to categorical data analysis

The weighted least squares method of Grizzle, Starmer and Koch is a common and convenient way to analyze categorical data. Tests of significance based on this method are typically performed by calculating a quadratic form which is asymptotically distrib-uted as a chi-square random variable under an appropriate null hy- pothesis.

This paper explores the validity of this distributional approximation for small samples in terms of the accuracy and power associated with this chi-square statistic. We report simulation results from the weighted least squares method applied to balanced factorial experiments, The simulations which constitute the majorpart of this work also provide insight into the relative performance of several response functions.     

On the asymptotic properties of fuzzy c-means cluster prototypes as estimators of mixture subpopulation centers

Several computational studies suggest that the fuzzy c-means (FCM) clustering scheme may be used successfully in some cases to obtain estimates for the parameters of a statistical mixture (e.g., for a mixture of normal distributions). While these (limited) simulation results for the fuzzy c-means approach support this hypothesis, we provide herein an example that shows that the FCM cluster prototypes cannot generally be statistically consistent estimators of the centers (means) of any univariate mixture having symmetric component distributions     

A note on the asymptotic distribution of mardia's measure of multivariate kurtosis

Weak convergence results are used to investigate asymptotic properties of Mardia's measure of multivariate kurtosis in the context of assessing multivariate normality.     

Tests concerning two non-isotropic principal components

Kshirsagar & Gupta (1965) proposed test criteria for testing the null hypothesis that a p x p covariance matrix, with known smallest latent root of multiplicity p-2. has its two non-isotropic principal components in specified directions. Simula-tion results indicate that the power functions of these test criteria lack some desirable properties. However, another statis-tic discussed by Kshirsagar and Gupta is shown to have a power-function which performs quite well. The situation in which the smallest latent root of multiplicity p-2 is unknown, is also considered.     

Model specification tests against non-nested alternatives

Non-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical 1iterature which has evolved from the fundamental work of Cox (1961, 1962) is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.