Arbeitspapier

Asymptotic optimality of the quasi-score estimator in a class of linear score estimators

we prove that the quasi-score estimator in a mean-variance model is optimal in the class of (unbiased) linear score estimators, in the sense that the difference of the asymptotic covariance matrices of the linear score and quasi-score estimator is positive semi-definite. We also give conditions under which this difference in zero or under which it is positive definite. This result can be applied to measurement error models where it implies that the quasi-score estimator is asymptotically more efficient than the corrected score estimator.

Language: Englisch

Bibliographic citation: Series: Discussion Paper ; No. 477

Event: Geistige Schöpfung

(who): Kukush, Alexander
Schneeweiss, Hans

Event: Veröffentlichung

(who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

(where): München

(when): 2006

DOI: doi:10.5282/ubm/epub.1845

Handle: http://hdl.handle.net/10419/31040

URN: urn:nbn:de:bvb:19-epub-1845-8

Last update: 10.03.2025, 11:46 AM CET

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Arbeitspapier

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Kukush, Alexander
Schneeweiss, Hans
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

Time of origin

2006

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Asymptotic optimality of the quasi-score estimator in a class of linear score estimators

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