Name
Abstract | ||
|---|---|---|
This paper presents a novel framework for maximum likelihood (ML) estimation in skew-t factor analysis (STFA) models in the presence of missing values or nonresponses. As a robust extension of the ordinary factor analysis model, the STFA model assumes a restricted version of the multivariate skew-t distribution for the latent factors and the unobservable errors to accommodate non-normal features such as asymmetry and heavy tails or outliers. An EM-type algorithm is developed to carry out ML estimation and imputation of missing values under a missing at random mechanism. The practical utility of the proposed methodology is illustrated through real and synthetic data examples. |
Year | Venue | Field |
|---|---|---|
2017 | Statistical Methods and Applications | Econometrics,Data mining,Multivariate statistics,Outlier,Synthetic data,Skew,Missing data,Imputation (statistics),Statistics,Asymmetry,Unobservable,Mathematics |
DocType | Volume | Issue |
Journal | 26 | 4 |
Citations | PageRank | References |
2 | 0.39 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wan-Lun Wang | 1 | 47 | 6.48 |
| Min Liu | 2 | 56 | 16.44 |
| Tsung I. Lin | 3 | 190 | 22.84 |