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Colloquium - 28 Oct 1999

Department of Statistics
Texas A&M University

ABSTRACT: Latent variable analysis is a statistical approach for modeling the underlying structure in multivariate data in terms of a smaller number of latent variables or factors. Factor analysis has been widely used in the social and behavioral sciences and in other areas of application, but can be an equally attractive approach for the modeling of environmental, agricultural, or ecological data. For such situations, a systematic approach for modeling the underlying structure of geo-referenced vector observations is proposed. Statistical inference procedures for model checking and building are discussed. We derive a condition under which a simple and practical inference procedure is valid without specifying the form of distributions and factor covariance functions. Alternative estimation and inference procedures are presented and compared. The multivariate prediction problem is also discussed, and a procedure combining the latent variable modeling and a measurement-error-free kriging technique is introduced. Simulation results and an example using agricultural data are presented.

Refreshments will be served in the Blocker Building, Room 447, at 3:30 p.m.

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