The Population-Sample Decomposition Method : A Distribution-Free Estimation Technique for Minimum Distance Parameters

I. Introduction to the Population-Sample Decomposition Approach -- I.1 The linear statistical model -- I.2 Minimum distance parameters subject to minimal model assumptions -- II. The Estimation of Linear Relations; The Sample Part of PSD -- II.1 Method of moments and asymptotic distribution theory -- II.2 Asymptotic estimation of covariance functions -- III. Principal Relations -- III.1 Basic formulation of the principal relations -- III.2 The distance matrix Q -- III.3 Simultaneous equations systems -- III.4 Seemingly unrelated regressions -- III.5 Restricted seemingly unrelated regressions -- III.6 Canonical correlation analysis -- IV. Principal Factors -- IV.1 Basic formulation of principal factors -- IV.2 Principal relations versus principal factors -- IV. 3 Principal components analysis -- V. Goodness-of-Fit Measures -- V. 1 Coefficients of multiple correlation and angles between random vectors -- V.2 Coefficients of linear association for principal relations and principal factors -- V.3 Coefficients of linear association for simultaneous equations systems -- V.4 Coefficients of linear association for seemingly unrelated regressions -- VI. Review -- VI.1 A schematic representation of the parameters -- VI.2 List of notation and summary of results -- VII. Computational Aspects of the Population-Sample Decomposition -- VII.1 Fourth-order central moments -- VII.2 Pre- and post-multiplication of V by the gradient matrix -- VII.3 The PSD method in practice -- Preliminaries on matrix algebra -- References -- Author Index.