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Title: Who's Afraid of Reduced-Rank Parameterizations of Multivariate Models? Theory and Example

Citation Type: Miscellaneous

Publication Year: 2004

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Abstract: Reduced-rank restrictions can add useful parsimony to coefficient matrices of multivariate models, but their use is limited by the daunting complexity of the methods and their theory. The present work takes the easy road, focusing on unifying themes andsimplified methods. For Gaussian and non-Gaussian (GLM, GAM, etc.) multivariate models, the present work gives a unified, explicit theory for the general asymptotic (normal) distribution of maximum likelihood estimators (MLE). MLE can be complexand computationally difficult, but we show a strong asymptotic equivalence between MLE and a relatively simple minimum (Mahalanobis) distance estimator. The latter method yields particularly simple tests of rank, and we describe its asymptotic behaviorin detail. We also examine the methods performance in simulation and via analytical and empirical examples.

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Authors: Zemcik, Petr; Gilbert, Scott

Publisher: CERGE-EI (Czech Republic)

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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