Another Look at the Naive Estimator in a Regression Model.
Aspects of analysis of multivariate failure time data.
Multivariate failure time data arise in various forms including recurrent event data when individuals are followed to observe the sequence of occurrences of a certain type of event; correlated failure time when an individual is followed for the occurrence of two or more types of events for which the individual is simultaneously at risk, or when distinct individuals have depending event times; or more complicated multistate processes where individuals may move among a number of discrete states over...
Asymptotic behaviour of the quadratic measure of deviation of multivariate density estimates
Asymptotic covariances for the generalized gamma distribution
The five-parameter generalized gamma distribution is one of the most flexible distributions in statistics. In this note, for the first time, we provide asymptotic covariances for the parameters using both the method of maximum likelihood and the method of moments.
Asymptotic efficiency and robustness of -estimators
Asymptotic properties of the growth curve model with covariance components
We consider a multivariate regression (growth curve) model of the form , , , where and ’s are unknown scalar covariance components. In the case of replicated observations, we derive the explicit form of the locally best estimators of the covariance components under normality and asymptotic confidence ellipsoids for certain linear functions of the first order parameters estimating simultaneously the first and the second order parameters.
Asymptotical confidence region in a replicated mixed linear model with an estimated covariance matrix
Asymptotically minimax estimation of a constrained Poisson vector via polydisc transforms
Asymptotically optimal estimation in linear regression in the case of violation of some classical assumptions.
Bad luck in quadratic improvement of the linear estimator in a special linear model
The paper concludes our investigations in looking for the locally best linear-quadratic estimators of mean value parameters and of the covariance matrix elements in a special structure of the linear model (2 variables case) where the dispersions of the observed quantities depend on the mean value parameters. Unfortunately there exists no linear-quadratic improvement of the linear estimator of mean value parameters in this model.
Bayes unbiased estimation in a model with three variance components
In the paper necessary and sufficient conditions for the existence and an explicit expression for the Bayes invariant quadratic unbiased estimate of the linear function of the variance components are presented for the mixed linear model , , , with three unknown variance components in the normal case. An application to some examples from the analysis of variance is given.
Bayesian estimation of the intraclass correlation coefficients in the mixed linear model
The method of determining Bayesian estimators for the special ratios of variance components called the intraclass correlation coefficients is presented. The exact posterior distribution for these ratios of variance components is obtained. The approximate posterior mean of this distribution is also derived. All computations are non-iterative and avoid numerical integration.
Changepoint estimation for dependent and non-stationary panels
The changepoint estimation problem of a common change in panel means for a very general panel data structure is considered. The observations within each panel are allowed to be generally dependent and non-stationary. Simultaneously, the panels are weakly dependent and non-stationary among each other. The follow up period can be extremely short and the changepoint magnitudes may differ across the panels accounting also for a specific situation that some magnitudes are equal to zero (thus, no jump...
Classifiers for doubly multivariate data
This paper proposes new classifiers under the assumption of multivariate normality for multivariate repeated measures data (doubly multivariate data) with Kronecker product covariance structures. These classifiers are especially useful when the number of observations is not large enough to estimate the covariance matrices, and thus the traditional classifiers fail. The quality of these new classifiers is examined on some real data. Computational schemes for maximum likelihood estimates of required...
Colinéarité et instabilité numérique dans le modèle linéaire
Colinearité et Instabilité Numérique dans le Modèle Linéaire
In this paper we give the expression of the multiple correlation coefficient in a linear model according to the coefficients of correlation. This expression makes it possible to analyze from a numerical point of view the instability of estimates in the case of collinear explanatory variables in the linear model or in the autoregressive model. This numerical approach, that we show on two examples, thus supplements the usual approach of the quasi colinearity, founded on the statistical properties...
Combined Unbiased Estimators Based on Q-Reduced Model.
Combining multivariate estimators of the mean vector
Meta-analysis is a standard statistical method used to combine the conclusions of individual studies that are related and the results of single study alone can not answered to deal with issues. The data are summarized by one or more outcome measure estimates along with their standard errors. The multivariate model and the variations between studies are not considered in most articles. Here we discuss multivariate effects models: a multivariate fixed effects model and a multivariate random effects...
Comment on C. R. Rao's MINQUE for replicated observations
Complete and sufficient statistics and perfect families in orthogonal and error orthogonal normal models
We will discuss orthogonal models and error orthogonal models and their algebraic structure, using as background, commutative Jordan algebras. The role of perfect families of symmetric matrices will be emphasized, since they will play an important part in the construction of the estimators for the relevant parameters. Perfect families of symmetric matrices form a basis for the commutative Jordan algebra they generate. When normality is assumed, these perfect families of symmetric matrices will ensure...