Predictive distribution for polytomous logistic models with nonlinear link functions.
Probability distribution of the multivariate nonlinear least squares estimates
Problems of an additional experiment
Problems of sensitiveness and linearization in a determination of isobestic points
Projection pursuit quadratic regression - the normal case
The model of quadratic regression is studied by means of the projection pursuit method. This method leads to a decomposition of the matrix of quadratic regression, which can be used for an estimation of this matrix from the data observed.
Properly recorded estimate and confidence regions obtained by an approximate covariance operator in a special nonlinear model
The properly recorded standard deviation of the estimator and the properly recorded estimate are introduced. Bounds for the locally best linear unbiased estimator and estimate and also confidence regions for a linearly unbiasedly estimable linear functional of unknown parameters of the mean value are obtained in a special structure of nonlinear regression model. A sufficient condition for obtaining the properly recorded estimate in this model is also given.
Properties of the generalized nonlinear least squares method applied for fitting distribution to data
We introduce and analyze a class of estimators for distribution parameters based on the relationship between the distribution function and the empirical distribution function. This class includes the nonlinear least squares estimator and the weighted nonlinear least squares estimator which has been used in parameter estimation for lifetime data (see e.g. [6, 8]) as well as the generalized nonlinear least squares estimator proposed in [3]. Sufficient conditions for consistency and asymptotic normality...
Propriétés de moindres carrés de la matrice de covariance d'un vecteur aléatoire et applications statistiques
Pyrrho's Lemma, or have it your Way.
QML estimators in linear regression models with functional coefficient autoregressive processes.
Quadratic estimations in mixed linear models
In the paper four types of estimations of the linear function of the variance components are presented for the mixed linear model with expectation and covariance matrix .
Quadratic regression models
Quadratic unbiased estimation of nonstandard parametric functions
Quelques alternatives à la régression classique dans le cas de la colinéarité
Quelques modèles probabilistes sous-jacents à la théorie des tests psychologiques
Random thresholds for linear model selection
A method is introduced to select the significant or non null mean terms among a collection of independent random variables. As an application we consider the problem of recovering the significant coefficients in non ordered model selection. The method is based on a convenient random centering of the partial sums of the ordered observations. Based on L-statistics methods we show consistency of the proposed estimator. An extension to unknown parametric distributions is considered. Simulated examples...
Rank conditions for estimability of covariances
Rank Test for Testing Randomness of a Regression Coefficient in a Linear Regression Model.
Rank tests in regression model based on minimum distance estimates
In this paper a new rank test in a linear regression model is introduced. The test statistic is based on a certain minimum distance estimator, however, unlike classical rank tests in regression it is not a simple linear rank statistic. Its exact distribution under the null hypothesis is derived, and further, the asymptotic distribution both under the null hypothesis and the local alternative is investigated. It is shown that the proposed test is applicable in measurement error models. Finally, a...
Rank theory approach to ridge, LASSO, preliminary test and Stein-type estimators: Comparative study
In the development of efficient predictive models, the key is to identify suitable predictors for a given linear model. For the first time, this paper provides a comparative study of ridge regression, LASSO, preliminary test and Stein-type estimators based on the theory of rank statistics. Under the orthonormal design matrix of a given linear model, we find that the rank based ridge estimator outperforms the usual rank estimator, restricted R-estimator, rank-based LASSO, preliminary test and Stein-type...