Locally best quadratic estimators
Locally Minimax Test of Independence in Elliptically Symmetrical Distributions with Additional Observations.
Locally Minimax Tests for a Multinormal Data Problem.
Logically orientated cluster analysis
Logistic discrimination with many variables.
Longitudinal K-sets analysis using a dummy time variable.
Machine fault diagnosis and condition prognosis using classification and regression trees and neuro-fuzzy inference system
Making use of incomplete observations for regression in bivariate normal model
Two estimates of the regression coefficient in bivariate normal distribution are considered: the usual one based on a sample and a new one making use of additional observations of one of the variables. They are compared with respect to variance. The same is done for two regression lines. The conclusion is that the additional observations are worth using only when the sample is very small.
Marginal problem, statistical estimation, and Möbius formula
A solution to the marginal problem is obtained in a form of parametric exponential (Gibbs–Markov) distribution, where the unknown parameters are obtained by an optimization procedure that agrees with the maximum likelihood (ML) estimate. With respect to a difficult performance of the method we propose also an alternative approach, providing the original basis of marginals can be appropriately extended. Then the (numerically feasible) solution can be obtained either by the maximum pseudo-likelihood...
Markov bases of conditional independence models for permutations
The L-decomposable and the bi-decomposable models are two families of distributions on the set of all permutations of the first positive integers. Both of these models are characterized by collections of conditional independence relations. We first compute a Markov basis for the L-decomposable model, then give partial results about the Markov basis of the bi-decomposable model. Using these Markov bases, we show that not all bi-decomposable distributions can be approximated arbitrarily well by...
Markov operators and n-copulas
We extend the definition of Markov operator in the sense of J. R. Brown and of earlier work of the authors to a setting appropriate to the study of n-copulas. Basic properties of this extension are studied.
Mathematical foundations of multivariate path analysis
Mathematical programming approaches to classification problems.
Matrices aléatoires : statistique asymptotique des valeurs propres
Matrix functions: Taylor expansion, sensitivity and error analysis
Matrix rank and inertia formulas in the analysis of general linear models
Matrix mathematics provides a powerful tool set for addressing statistical problems, in particular, the theory of matrix ranks and inertias has been developed as effective methodology of simplifying various complicated matrix expressions, and establishing equalities and inequalities occurred in statistical analysis. This paper describes how to establish exact formulas for calculating ranks and inertias of covariances of predictors and estimators of parameter spaces in general linear models (GLMs),...
Matrix variate Kummer-Dirichlet distributions.
Matrix-variate beta distribution.
Maximal association for the sum of squares of a contingency table
Maximal correlation in path analysis