Simulation-Based Bias Correction Methods for Complex Models
| Autor: | Stéphane Guerrier, Yanyuan Ma, Elise Dupuis-Lozeron, Maria-Pia Victoria-Feser |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Computer science Robust statistics Indirect Inference computer.software_genre 01 natural sciences Indirect inference Generalized latent variable model Weighted maximum likelihood estimators 010104 statistics & probability Two-step estimators 0502 economics and business Econometrics ddc:310 Bias correction Weighted maximum likelihood estimator 0101 mathematics Simulation based 050205 econometrics 05 social sciences Iterative bootstrap Estimator M-estimator Model complexity Generalized latent variable models Two-step estimator Data mining Statistics Probability and Uncertainty computer |
| Zdroj: | BASE-Bielefeld Academic Search Engine Journal of the American Statistical Association, Vol. 114 (2019) pp. 146-157 |
| ISSN: | 0162-1459 |
| DOI: | 10.6084/m9.figshare.5457418.v2 |
| Popis: | Along with the ever increasing data size and model complexity, an important challenge frequently encountered in constructing new estimators or in implementing a classical one such as the maximum likelihood estimator, is the computational aspect of the estimation procedure. To carry out estimation, approximate methods such as pseudo-likelihood functions or approximated estimating equations are increasingly used in practice as these methods are typically easier to implement numerically although they can lead to inconsistent and/or biased estimators. In this context, we extend and provide refinements on the known bias correction properties of two simulation-based methods, respectively, indirect inference and bootstrap, each with two alternatives. These results allow one to build a framework defining simulation-based estimators that can be implemented for complex models. Indeed, based on a biased or even inconsistent estimator, several simulation-based methods can be used to define new estimators that are both consistent and with reduced finite sample bias. This framework includes the classical method of the indirect inference for bias correction without requiring specification of an auxiliary model. We demonstrate the equivalence between one version of the indirect inference and the iterative bootstrap, both correct sample biases up to the order n− 3. The iterative method can be thought of as a computationally efficient algorithm to solve the optimization problem of the indirect inference. Our results provide different tools to correct the asymptotic as well as finite sample biases of estimators and give insight on which method should be applied for the problem at hand. The usefulness of the proposed approach is illustrated with the estimation of robust income distributions and generalized linear latent variable models. Supplementary materials for this article are available online. |
| Databáze: | OpenAIRE |
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