| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Estadual Campinas, Dept Stat, Campinas, SP - Brazil
[2] Univ Fed Pernambuco, Dept Stat, Recife, PE - Brazil
[3] Univ Connecticut, Dept Stat, Storrs, CT 06269 - USA
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | Journal of Applied Statistics; v. 45, n. 11, p. 2039-2066, 2018. |
| Web of Science Citations: | 1 |
| Abstract | |
In many studies, the data collected are subject to some upper and lower detection limits. Hence, the responses are either left or right censored. A complication arises when these continuous measures present heavy tails and asymmetrical behavior; simultaneously. For such data structures, we propose a robust-censored linear model based on the scale mixtures of skew-normal (SMSN) distributions. The SMSN is an attractive class of asymmetrical heavy-tailed densities that includes the skew-normal, skew-t, skew-slash, skew-contaminated normal and the entire family of scale mixtures of normal (SMN) distributions as special cases. We propose a fast estimation procedure to obtain the maximum likelihood (ML) estimates of the parameters, using a stochastic approximation of the EM (SAEM) algorithm. This approach allows us to estimate the parameters of interest easily and quickly, obtaining as a byproducts the standard errors, predictions of unobservable values of the response and the log-likelihood function. The proposed methods are illustrated through real data applications and several simulation studies. (AU) | |
| FAPESP's process: | 13/21468-0 - Measurement error-in-variables models for censored data using scale mixtures of skew-normal distributions |
| Grantee: | Aldo William Medina Garay |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |