期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 24, 期 1, 页码 183-204出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10618600.2014.889023
关键词
Ordinal variable; Probit model; Lasso
资金
- NSF [DMS-0805798, DMS-1106772, DMS-1159005, DMS-0748389]
- NIH [1RC1CA145444-0110, R01GM096194]
- Direct For Mathematical & Physical Scien [1106772, 1228164, 1159005] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1407698, 1545277] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1106772, 1228164, 1159005] Funding Source: National Science Foundation
This article considers a graphical model for ordinal variables, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on datasets on movie ratings and an educational survey.
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