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Censored regression models with double exponential error distributions: an iterattive estimation procedure based on medians for correcting bias.

Carmen Anido, Teófilo Valdés (2000)

Revista Matemática Complutense

In this paper, we consider a simple iterative estimation procedure for censored regression models with symmetrical exponential error distributions. Although each step requires to impute the censored data with conditional medians, its tractability is guaranteed as well as its convergence at geometrical rate. Finally, as the final estimate coincides with a Huber M-estimator, its consistency and asymptotic normality are easily proved.

Characterization of admissible linear estimators under extended balanced loss function

Buatikan Mirezi, Selahattin Kaçıranlar (2021)

Kybernetika

In this paper, we study the admissibility of linear estimator of regression coefficient in linear model under the extended balanced loss function (EBLF). The sufficient and necessary condition for linear estimators to be admissible are obtained respectively in homogeneous and non-homogeneous classes. Furthermore, we show that admissible linear estimator under the EBLF is a convex combination of the admissible linear estimator under the sum of square residuals and quadratic loss function.

Characterization of the multivariate Gauss-Markoff model with singular covariance matrix and missing values

Wiktor Oktaba (1998)

Applications of Mathematics

The aim of this paper is to characterize the Multivariate Gauss-Markoff model $(M G M)$ as in () with singular covariance matrix and missing values. $M G M D P 2$ model and completed $M G M D P 2 Q$ model are obtained by three transformations $D$ , $P$ and $Q$ (cf. ()) of $M G M$ . The unified theory of estimation (Rao, 1973) which is of interest with respect to $M G M$ has been used. The characterization is reached by estimation of parameters: scalar $σ^{2}$ and linear combination $λ^{'} \bar{B}$ ( $\bar{B} = v e c B)$ as in (), (), () as well as by the model of the form () (cf. Th. )....

Choice of a Survival Model for Patients With a Brain Tumour.

B. de de Pereira (1981)

Metrika

Classification of linear estimators

Pavel Kovanic (1979)

Kybernetika

Colinéarité et instabilité numérique dans le modèle linéaire

Thierry Foucart (2000)

RAIRO - Operations Research - Recherche Opérationnelle

Colinearité et Instabilité Numérique dans le Modèle Linéaire

Thierry Foucart (2010)

RAIRO - Operations Research

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...

Combining forecasts using the least trimmed squares

Jan Ámos Víšek (2001)

Kybernetika

Employing recently derived asymptotic representation of the least trimmed squares estimator, the combinations of the forecasts with constraints are studied. Under assumption of unbiasedness of individual forecasts it is shown that the combination without intercept and with constraint imposed on the estimate of regression coefficients that they sum to one, is better than others. A numerical example is included to support theoretical conclusions.

Comparaison de quelques tests d'indépendance des erreurs dans le modèle linéaire

M. Benhelli, E. Lambert (1981)

Statistique et analyse des données

Computing the distribution of a linear combination of inverted gamma variables

Viktor Witkovský (2001)

Kybernetika

A formula for evaluation of the distribution of a linear combination of independent inverted gamma random variables by one-dimensional numerical integration is presented. The formula is direct application of the inversion formula given by Gil–Pelaez [gil-pelaez]. This method is applied to computation of the generalized $p$ -values used for exact significance testing and interval estimation of the parameter of interest in the Behrens–Fisher problem and for variance components in balanced mixed linear...

Computing the minimal efficiency of designs by a differentiable approximation of $Φ_{E_{k}}$ -optimality criteria.

Bušová, Lenka (2008)

Acta Mathematica Universitatis Comenianae. New Series

Confidence band for regression line with exponential distribution of errors

Karel Zvára (1995)

Kybernetika

Confidence regions in a multivariate regression model with constraints II

Lubomír Kubáček (2007)

Mathematica Slovaca

Consistency in the p-th mean of the least Squares Estimators in Linear Models

Demetrios Kaffes (1992)

Δελτίο της Ελληνικής Μαθηματικής Εταιρίας

Consistency of an estimate in linear regression with non-negative errors

Karel Zvára (1992)

Kybernetika

Consistency of linear and quadratic least squares estimators in regression models with covariance stationary errors

František Štulajter (1991)

Applications of Mathematics

The least squres invariant quadratic estimator of an unknown covariance function of a stochastic process is defined and a sufficient condition for consistency of this estimator is derived. The mean value of the observed process is assumed to fulfil a linear regresion model. A sufficient condition for consistency of the least squares estimator of the regression parameters is derived, too.

Consistency of the least weighted squares under heteroscedasticity

Jan Ámos Víšek (2011)

Kybernetika

A robust version of the Ordinary Least Squares accommodating the idea of weighting the order statistics of the squared residuals (rather than directly the squares of residuals) is recalled and its properties are studied. The existence of solution of the corresponding extremal problem and the consistency under heteroscedasticity is proved.

Consistency of the LSE in Linear regression with stationary noise

Guy Cohen, Michael Lin, Arkady Tempelman (2004)

Colloquium Mathematicae

We obtain conditions for L₂ and strong consistency of the least square estimators of the coefficients in a multi-linear regression model with a stationary random noise. For given non-random regressors, we obtain conditions which ensure L₂-consistency for all wide sense stationary noise sequences with spectral measure in a given class. The condition for the class of all noises with continuous (i.e., atomless) spectral measures yields also $L_{p}$ -consistency when the noise is strict sense stationary with...

Consistent Estimation of a Regression with Errors in the Variables.

H. Schneeweiß (1976)

Metrika

Contribuciones a la generalización del problema de compensación por grupos de Helmert-Pranis Pranievich.

Ioan Popescu (1988)

Revista Matemática de la Universidad Complutense de Madrid

The paper presents in a generalized form the problem of the geodetic network adjustment by the Helmert-Pranis Pranievich groups method (groups with junction points included or not). The adjustment problem, as well as the cofactor matrix derivation for the partial-independent and linkage unknowns, was completely formulated by transformed weight matrix definition and usage. A complete sequence of the computing stages for the geodetic networks divided into groups without junction points was given for...

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