Small Sample Properties of Parametric and Nonparametric Estimators in Q
by Dongryoen Park and Sangun Park.
Abstract:
In bioassay, the logit model is the most widely used parametric
model. However, the exact form of the response curve is usually
unknown and even very complicated, so it is likely that the true
model does not follow the logit model. Therefore, according to
well known asymptotic results, when the sample size is very
large, we should probably use nonparametric regression rather
than the logit model unless the exact form of the true response
curve is known. In practice, however, we can not increase the
sample size infinitely, so the asymptotic result would not be so
useful. In this paper, we would like to compare the small sample
properties of the logit model and the nonparametric estimator. As
the nonparametric method, we choose the locally weighted
quasi--likelihood estimator. A Monte Carlo study was done under
various circumstances, and it turned out that the locally
weighted quasi--likelihood estimator is very competitive in the
small sample situation.
Key Words:
Local quasi--likelihood, Logit model,
Nonparametric regression, Response curve, Small sample.
Authors:
Dongryeon Park, drpark@hanshin.ac.kr
Sangun Park, sangun@yonsei.ac.kr
Editor:
Avner Bar-Hen,avner@bar-hen.net
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