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Abstract Details
Activity Number:
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297
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #305426 |
Title:
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Estimating Infinite-Dimensional Parameters via Pointwise Estimation
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Author(s):
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Marco Carone*+ and Mark van der Laan
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Companies:
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University of California at Berkeley and University of California at Berkeley School of Public Health
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Address:
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Division of Biostatistics, Berkeley, CA, 94720, United States
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Keywords:
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infinite-dimensional parameter ;
pointwise estimation ;
targeted maximum likelihood estimation
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Abstract:
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Many important statistical tools are designed to estimate parameters of finite dimension. However, in many important applications, the statistical parameter lies in a space of infinite dimension; estimation of a survival function is a common example of such. In this work, we present a simple framework for constructing statistical procedures for estimating an infinite-dimensional parameter using estimators of its pointwise evaluation (a finite-dimensional parameter). We discuss the asymptotic properties of the resulting procedure, and we demonstrate how this approach, in conjunction with targeted maximum likelihood estimation, provides a formal framework for producing consistent and asymptotically efficient estimators of infinite-dimensional statistical parameters. We illustrate the merits of the approach through simulation studies.
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