Linear latent structure analysis: Mixture distribution models with linear constraints

Published

Journal Article

A new method for analyzing high-dimensional categorical data, Linear Latent Structure (LLS) analysis, is presented. LLS models belong to the family of latent structure models, which are mixture distribution models constrained to satisfy the local independence assumption. LLS analysis explicitly considers a family of mixed distributions as a linear space, and LLS models are obtained by imposing linear constraints on the mixing distribution. LLS models are identifiable under modest conditions and are consistently estimable. A remarkable feature of LLS analysis is the existence of a high-performance numerical algorithm, which reduces parameter estimation to a sequence of linear algebra problems. Simulation experiments with a prototype of the algorithm demonstrated a good quality of restoration of model parameters. © 2006 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Kovtun, M; Akushevich, I; Manton, KG; Tolley, HD

Published Date

  • January 1, 2007

Published In

Volume / Issue

  • 4 / 1

Start / End Page

  • 90 - 110

International Standard Serial Number (ISSN)

  • 1572-3127

Digital Object Identifier (DOI)

  • 10.1016/j.stamet.2006.04.001

Citation Source

  • Scopus