What You Always Wanted to Know About Censoring But Never Dared to Ask – Parameter Estimation for Censored Random Vectors
This article considers a wide class of censoring problems and presents a construction rule for an objective function. This objective function generalises the ordinary likelihood as well as particular “likelihoods” used for estimation in several censoring models. Under regularity conditions the maximiser of this generalised likelihood has all the properties of a maximum likelihood estimator: it is consistent and the respective root-n estimator is asymptotically efficient and normally distributed.
|Date of creation:||Jul 2003|
|Date of revision:|
|Publication status:||published in: Econometric Reviews, 2005, 24 (2), 195-217|
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- Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
- Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
- repec:oup:restud:v:67:y:2000:i:4:p:667-96 is not listed on IDEAS
- Nelson, Forrest D., 1977. "Censored regression models with unobserved, stochastic censoring thresholds," Journal of Econometrics, Elsevier, vol. 6(3), pages 309-327, November.
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