| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Sao Paulo IME USP, Inst Math & Stat, Rua Matao 1010, Cidade Univ, BR-05508090 Sao Paulo - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | STATISTICAL PAPERS; v. 60, n. 1, p. 89-104, FEB 2018. |
| Web of Science Citations: | 0 |
| Abstract | |
Adopting likelihood based methods of inference in the case of informative sampling often presents a number of difficulties, particularly, if the parametric form of the model that describes the sample selection mechanism is unknown, and thus requires application of some model selection approach. These difficulties generally arise either due to complexity of the model holding in the sample, or due to identifiability problems. As a remedy we propose alternative approach to model selection and estimation in the case of informative sampling. Our approach is based on weighted estimation equations, where the contribution to the estimation equation from each observation is weighted by the inverse probability of being selected. We show how weighted estimation equations can be incorporated in a Bayesian analysis, and how the full Bayesian significance test can be implemented as a model selection tool. We illustrate the efficiency of the proposed methodology by a simulation study. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 13/17746-5 - Bayesian Approach to Handling Informative Nonresponse |
| Grantee: | Anna Sikov |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |