Most methods for small-area estimation are based on composite estimators derived from design- or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although...

A national survey designed for estimating a specific population quantity is sometimes used for estimation of this quantity also for a small area, such as a province. Budget constraints do not allow a greater sample size for the small area, and so other means of improving estimation have to be...

The present paper proposes a model for the persistence of abnormal returns both at firm and industry levels, when longitudinal data for the profits of firms classiffied as industries are available. The model produces a two-way variance decomposition of abnormal returns: (a) at firm versus...

New challenges arise in data visualization when the research involves a sizable database. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots, such as the boxplot that are of limited use in small samples, offer great...

New challenges arise in data visualization when a sizable database is used in the analysis. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots such as the boxplot that are of limited use in small samples, offer great...

New challenges arise in data visualization when a sizable database is used in the analysis. With many data points, classical scatterplots are non-informative due to the cluttering of points. On the contrary, simple plots such as the boxplot that are of limited use in small samples, offer great...

Covariance structure analysis of nonnormal data is important because in practice all data are nonnormal. When applying covariance structure analysis to nonnormal data, it is generally assumed that the asymptotic covariance matrix Γ for the nonredundant terms in the sample covariance matrix S is...

It is shown that for any full column rank matrix X <Subscript>0</Subscript> with more rows than columns there is a neighborhood <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\mathcal{N}$</EquationSource> </InlineEquation> of X <Subscript>0</Subscript> and a continuous function f on <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\mathcal{N}$</EquationSource> </InlineEquation> such that f(X) is an orthogonal complement of X for all X in <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\mathcal{N}$</EquationSource> </InlineEquation>. This is used to derive a distribution free...</equationsource></inlineequation></equationsource></inlineequation></subscript></equationsource></inlineequation></subscript>