Estimation of variance components in staggered nested designs

SUMMARY Variance components are estimated by two different methods for a general p stage random-effects staggered nested design. In addition to estimation from an analysis of variance, a new approach is introduced. The main features of this new technique are its simplicity and its ability to yield non-negative estimates of the variance components. The performances of the two procedures are compared using simulation and the meansquared-error criterion.     

Prediction intervals for growth curves

The growth curve model Y^n×p = A^{n×p ξ mtimes;k}B^k×p+ E^nxp, where Y is an observation matrix, &sigma is a matrix of unknown parameters, A is a known matrix of rank m, B is a known matrix of rank k with 1'= (1, …, 1) as its first row, and the rows of E are independent each distributed as N^p(0,Σ,) is considered. The problem of constructing the prediction intervals for future observations using the above model is considered and approximate intervals assuming different structures on σ are derived. The results are illustrated with several data sets.     

Efficient Algorithms for a Scheduling Problem and its Applications to Illicit Drug Market Crackdowns

We give polynomial time algorithms for a job scheduling problem. By duality we transform a special case of the drug market crackdown schedulingproblem to the above job scheduling problem and thus derive polynomial timealgorithms to the second problem. Finally, using the algorithm for the specialcase, we develop a quasipolynomial time approximation algorithm for thegeneral case of the drug market crackdown scheduling problem with monomialcost functions.     

On the q-Weibull Distribution and Its Applications

The q-Weibull distribution is a stretched model for Weibull distribution, obtained by introducing a new pathway parameter q, which facilitates a slow transition to the Weibull as q → 1. In this article, we make a detailed study of the properties of the q-Weibull distribution and we apply it to a data on cancer remission times for which this distribution is a better fit than Weibull. Results relating to reliability properties, estimation of parameters, and applications in stress-strength analysis are also obtained.     

Detection of outliers in bivariate time series data

In this article, we use the influence function matrix of auto and cross-correlations of a bivariate (multivariate) time series for detecting the outliers. The multivariate analog of the graphical method of Chernick et. al. (1982), to detect outliers and partial outliers is presented. A simulation study illustrating the method is also given.     

On regression method for estimating a population proportion

In this paper the regression method of estimation has been discussed for the estimation of proportion of the units possessing the given characteristic. An unbiased regression type estimator has also been discussed. An unbiased estimator is given for estimating the common MSE/variance of the estimators discussed. The results have also been extended to deal with the populations of a peculiar nature.     

Detection of influential observations in multivariate regression

Several methods have been suggested, in the literature, to detect influential observations from the data fitting usual linear model y=X???+???, ???∽N(0, ???2I). Recently, Chatterjee & Hadi (1986) have reviewed most of these available methods and described the inter-relationships between them. In this article, we extend some of these methods to the case of multivariate regression data. We consider several data sets to illustrate the methods.     

Statistical tests for random effects in staggered nested designs

In this paper, we present certain statistical tests under a staggered nested design set-up, for the hypotheses that certain variance components are zero. To do so, the particular variance-covariance, structure induced by the staggering is exploited and certain results of multivariate analysis are used. In most problems, the test statistics can be easily computed. An example is provided for illustration and some power computations for comparison of test statistics are shown.