AbstractThis paper provides further contributions to the theory of linear sufficiency and linear completeness. The notion of linear sufficiency was introduced by Baksalary and Kala (1981, Ann. Statist. 9, 913–916) and Drygas (in press, Sankhya) with respect to the linear model Ey = Xβ, var y = V. In addition to correcting an inadequate proof of [8], the relationship to an earlier definition and to the theory of linear prediction is also demonstrated. Moreover, the notion is extended to the model Ey = Xβ, var y = δ2V. Its connection with sufficiency under normality is investigated. An example illustrates the results
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
Admissible and linearly sufficient estimators in linear model are identified as general ridge estima...
Linear models are a type of mathematical model commonly used by statisticians in order to capture th...
In this paper we consider the linear sufficiency of Fy for Xβ, for Zu and for Xβ + Zu, when dealing ...
AbstractConsider a general linear model Y=Xβ+Z where CovZ may be known only partially. We investigat...
Consider a general linear model $Y=X\beta+Z$, where $\text{Cov}\,Z$ may be known only partially. We ...
AbstractIn the linear model Y = Xβ + u the question arises when a linear transformation z = Ly conta...
AbstractThe notion of linear sufficiency for the whole set of estimable functions in the general Gau...
This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It co...
A linear statistic Fy is called linearly sufficient for the estimable parametric function of X*β und...
We contrast two approaches to the prediction of latent variables in the model of factor analysis. Th...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
In this paper we present a new (slightly more general) formulation of the concept of L-sufficiency d...
AbstractNotions of linear sufficiency and quadratic sufficiency are of interest to some authors. In ...
The book is based on several years of experience of both authors in teaching linear models at variou...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
Admissible and linearly sufficient estimators in linear model are identified as general ridge estima...
Linear models are a type of mathematical model commonly used by statisticians in order to capture th...
In this paper we consider the linear sufficiency of Fy for Xβ, for Zu and for Xβ + Zu, when dealing ...
AbstractConsider a general linear model Y=Xβ+Z where CovZ may be known only partially. We investigat...
Consider a general linear model $Y=X\beta+Z$, where $\text{Cov}\,Z$ may be known only partially. We ...
AbstractIn the linear model Y = Xβ + u the question arises when a linear transformation z = Ly conta...
AbstractThe notion of linear sufficiency for the whole set of estimable functions in the general Gau...
This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It co...
A linear statistic Fy is called linearly sufficient for the estimable parametric function of X*β und...
We contrast two approaches to the prediction of latent variables in the model of factor analysis. Th...
The Neyman—Pearson Lemma introduced the concept of optimality into statistics. The derivation of opt...
In this paper we present a new (slightly more general) formulation of the concept of L-sufficiency d...
AbstractNotions of linear sufficiency and quadratic sufficiency are of interest to some authors. In ...
The book is based on several years of experience of both authors in teaching linear models at variou...
. A general linear model can be written as Y = XB 0 + U , where Y is an N \Theta p matrix of obser...
Admissible and linearly sufficient estimators in linear model are identified as general ridge estima...
Linear models are a type of mathematical model commonly used by statisticians in order to capture th...