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Profile Likelihood and Conditionally Parametric Models
Open accessAbstract
In this paper, we outline a general approach to estimating the parametric component of a semiparametric model. For the case of a scalar parametric component, the method is based on the idea of first estimating a one-dimensional subproblem of the original problem that is least favorable in the sense of Stein. The likelihood function for the scalar parameter along this estimated subproblem may be viewed as a generalization of the profile likelihood for the problem. The scalar parameter is then estimated by maximizing this "generalized profile likelihood." This method of estimation is applied to a particular class of semiparametric models, where it is shown that the resulting estimator is asymptotically efficient.
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Nadaraya, E. A. (1964) · Theory of Probability & Its Applications · doi:10.1137/1109020 · Cited by 3,462
Cited by
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Xia, Y., Tong, H., Li, W. K., & Zhu, L.-X. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.03411 · Cited by 820
Efficient Estimation of Models with Conditional Moment Restrictions Containing Unknown Functions
Ai, C., & Chen, X. (2003) · Econometrica · doi:10.1111/1468-0262.00470 · Cited by 726
Murphy, S. A., & Van Der Vaart, A. W. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474219 · Cited by 712
Double/debiased machine learning for treatment and structural parameters
Newey, W. K., Hansen, C., Duflo, E., Robins, J., Chernozhukov, V., Demirer, M., & Chetverikov, D. (2017) · doi:10.1920/wp.cem.2017.2817 · Cited by 486
Metadata
- Authors
- Severini, T. A., & Wong, W. H.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1992-12-01
- Volume
- 20
- Issue
- 4
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2046977371
- Cited by
- 297
- References
- 1
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
