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We propose a new parsimonious version of the classical multivariate normal linear model, yielding a maximum likelihood estimator (MLE) that is asymptotically less variable than the MLE based on the usual model. Our approach is based on the construction of a link between the mean function and the covariance matrix, using the minimal reducing subspace of the latter that accommodates the former. T...

Multivariate statistical analysis is an important data analysis technique that has found applications in various areas. In this paper, we study some multivariate statistical analysis methods in Secure 2-party Computation (S2C) framework illustrated by the following scenario: two parties, each having a secret data set, want to conduct the statistical analysis on their joint data, but neither par...

This article studies estimation of partially linear hazard regression models for multivariate survival data. A profile pseudo–partial likelihood estimation method is proposed under the marginal hazard model framework. The estimation on the parameters for the linear part is accomplished by maximization of a pseudo–partial likelihood profiled over the nonparametric part. This enables us to obtain...

We consider linear models where d potential causes X1, . . . , Xd are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix ΣXX of X...

Multivariate regression is used in a wide variety of fields as a modeling and classification tool. In this paper we investigate its potential as means of intrusion detection. We demonstrate that intrusion detectors constructed by multivariate linear regression can achieve high accuracy with very short training and testing times, and conclude with a discussion of future research directions.

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has been recently received great attention. For these studies, it is essential to use a small subset of informative SNPs (tag SNPs) accurately representing the rest of the SNPs. Tagging can achieve budget savings by genotyping only a limited number of SNPs and computationally inferri...

We consider the problem of combining the outputs of several classiiers trained independently to perform a discrimination task, in order to improve the prediction accuracy of individual classiiers. We brieey describe the multivariate linear regression model which has already been implemented successfully for that purpose and we study its capacity, using generalizations of the notion of VC dimens...

We introduce a general formulation for dimension reduction and coefficient estimation in the multivariate linear model. We argue that many of the existing methods that are commonly used in practice can be formulated in this framework and have various restrictions. We continue to propose a new method that is more flexible and more generally applicable. The method proposed can be formulated as a ...