Asymptotic Theory of Sequential Estimation: Differential Geometrical Approach
Sequential estimation continues observations until the observed sample satisfies a prescribed criterion. Its properties are superior on the average to those of nonsequential estimation in which the number of observations is fixed a priori. A higher-order asymptotic theory of sequential estimation is...
Veröffentlicht in: | The Annals of Statistics. - Institute of Mathematical Statistics. - 19(1991), 2, Seite 961-981 |
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1. Verfasser: | |
Weitere Verfasser: | , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
1991
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Zugriff auf das übergeordnete Werk: | The Annals of Statistics |
Schlagworte: | Asymptotic theory conformal transformation covariance stabilization differential geometry higher-order asymptotics sequential estimation statistical curvature stopping rule Mathematics Behavioral sciences |
Zusammenfassung: | Sequential estimation continues observations until the observed sample satisfies a prescribed criterion. Its properties are superior on the average to those of nonsequential estimation in which the number of observations is fixed a priori. A higher-order asymptotic theory of sequential estimation is given in the framework of geometry of multidimensional curved exponential families. This gives a design principle of the second-order efficient sequential estimation procedure. It is also shown that a sequential estimation can be designed to have a covariance stabilizing effect at the same time. |
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ISSN: | 00905364 |