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Asymptotic Properties of Conditional Maximum-Likelihood Estimators
Abstract
Summary: The problem of obtaining consistent estimates for structural parameters in the presence of infinitely many incidental parameters was discussed first by Neyman and Scott (1948). In this paper a maximum-likelihood method based on the conditional distribution given minimal sufficient statistics for the incidental parameters is suggested. It is proved that conditional maximum-likelihood estimates in the regular case are consistent and asymptotically normally distributed with a simple asymptotic variance. The efficiency problem of this new estimator is discussed in particular with respect to some situations with ancillary information.
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Cites
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Fixed and Random Effects in Stochastic Frontier Models
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Metadata
- Authors
- Andersen, E. B.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1970-07-01
- Volume
- 32
- Issue
- 2
- Pages
- 283-301
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2117450242
- Cited by
- 832
- References
- 16
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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