A note on tolerance regions for random vectors and best linear predictors

When a (p+q)-variate column vector (x′,y′)′ has a (p+q)-variate normal density with mean vector (μ₁,μ₂) and covariance matrix Ω, unknown, Schervish (1980) obtains prediction intervals for the linear functions of a future y, given x. He bases the prediction interval on the F-distribution. However, for a specified linear function the statistic to be used is Student's t, since the prediction intervals based on t are shorter than those based on F. Similar results hold for the multivariate linear regression model.     

ON THE DISTRIBUTION OF THE REGRESSION COEFFICIENT MATRIX OF A NORMAL DISTRIBUTION

In this paper we generalize a result of Kshirsagar's (1960, pp. 83–84) on the distribution of the regression coefficient matrix for a multivariate normal population.     

On the maximal invariance of manova step down procedure statistics

Subbaiah and Mudhol kar (1978) remark the general mu1tivariate linear hypothesis testing step down procedure statistics do not appear to be maximal invariants under nonsingular lower triangular matrix transformations of the original variates. This paper proves the maximal invariance of these statistics. The invariance results are essential to study the power functions of the step down procedures for MANOVA problems. An example is given to show that such power function studies are very involved.     

On tests of independence between stochastic regressors and disturbances

In this paper two tests for testing the hypothesis that the stochastic endogenous regressors in a structural equation, embedded in a system of simultaneous structural equations, are independent of the disturbances in that equation are presented.     

On a gmanova model likelihood ratio test criterion

For the generalized MANOVA (GMANOVA) model of Potthoff and Roy (1964), X = BξA + E, Khatri (1966) derives the likelihood ratio test criterion for test-ing the composite double linear null hypothesis CξV = 0, C,V known. This criterion plays an important role in statistics, and several authors have recently studied its further properties. However, Khatri's (1966) de-reviation of the distribution of this criterion is involved. By noting that the GMANOVA model is re-stricted MANOVA model, this paper presents an alter-native simple derivation of the distribution of this criterion. The derivation is based on the generalized Sverdrup's lemma, Kabe (1965).     

Manova double linear hypothesis with double linear restrictions

Given the usual normal multivariate linear regression model Y = BX + E, with B subjected to double linear restrictions GBF' = T, a likelihood ratio test criterion for testing the composite linear null hypothesis HBJ' = U; G, F, T, H, J, U specified, is provided. The applications of such tests are discussed by Timm (1980).     

Combinatorial analysis for route first-cluster second vehicle routing

Two Route first-cluster second vehicle routing algorithms are contrasted in the first section of the paper. Next, the ‘large’ number of feasible solutions to a multiple travelling salesman problem is established given that each salesman can visit any number of customers in a stated range. An approximate expression is given for the ‘small’ fraction of this solution space searched by a route first-cluster second vehicle routing heuristic. Nevertheless, this heuristic is seen to be a very efficient means of searching its solution space.