Let <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\mathcal{M }_{\underline{i}}$$</EquationSource> </InlineEquation> be an exponential family of densities on <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$[0,1]$$</EquationSource> </InlineEquation> pertaining to a vector of orthonormal functions <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$$b_{\underline{i}}=(b_{i_1}(x),\ldots ,b_{i_p}(x))^\mathbf{T}$$</EquationSource> </InlineEquation> and consider a problem of estimating a density <InlineEquation ID="IEq6"> <EquationSource Format="TEX">$$f$$</EquationSource> </InlineEquation> belonging to such family for...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

In this paper, a focused vector information criterion for model selection and model averaging is considered for the linear model with missing response. Based on the focused information criterion of Hjort and Claeskens (J Am Stat Assoc 98:879–945, <CitationRef CitationID="CR12">2003</CitationRef>) and imputation idea, a frequentist model...</citationref>

In this paper we study the relationship between regression analysis and a multivariate dependency measure. If the general regression model Y=f([InlineMediaObject not available: see fulltext.]) holds for some function f, where 1≤i <Subscript>1</Subscript> i <Subscript>2</Subscript>···i <Subscript> m </Subscript> ≤k, and X <Subscript>1</Subscript>,...,X <Subscript> k </Subscript> is a set of possible...</subscript></subscript></subscript></subscript></subscript>