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Approximate Likelihood Inference in Generalized Linear Models with Censored Covariates
| Content Provider | Scilit |
|---|---|
| Author | Teimouri, Mahdi Sinha, Sanjoy K |
| Copyright Year | 2022 |
| Description | In many surveys and clinical trials, we obtain measurements on covariates or biomarkers that are left-censored due to the limit of detection. In such cases, it is necessary to correct for the left-censoring when studying covariate effects in regression models. The expectation-maximization (EM) algorithm is widely used for the likelihood inference in generalized linear models with censored covariates. The EM method, however, requires intensive computation involving high-dimensional integration with respect to the covariates when the dimension of the censored covariates is large. To reduce such computational difficulties, we propose and explore a Monte Carlo EM method based on the Metropolis algorithm. The finite-sample properties of the proposed estimators are studied using Monte Carlo simulations. An application is also provided using actual data obtained from a health and nutrition examination survey. Journal of Statistical Research 2021, Vol. 55, No. 2, pp. 359-375 |
| Ending Page | 375 |
| Page Count | 17 |
| Starting Page | 359 |
| ISSN | 0256422X |
| DOI | 10.3329/jsr.v55i2.58810 |
| Journal | Journal of Statistical Research |
| Issue Number | 2 |
| Volume Number | 55 |
| Language | English |
| Publisher | Bangladesh Academy of Sciences |
| Publisher Date | 2022-03-30 |
| Access Restriction | Open |
| Subject Keyword | Mathematical Social Sciences Monte Carlo |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Demography Social Sciences Modeling and Simulation |
