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Local maximum likelihood estimation and inference
Author(s) -
Fan J.,
Farmen M.,
Gijbels I.
Publication year - 1998
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00142
Subject(s) - nonparametric statistics , maximum likelihood sequence estimation , statistics , parametric statistics , restricted maximum likelihood , mathematics , inference , maximum likelihood , nonparametric regression , logistic regression , econometrics , computer science , artificial intelligence
Local maximum likelihood estimation is a nonparametric counterpart of the widely used parametric maximum likelihood technique. It extends the scope of the parametric maximum likelihood method to a much wider class of parametric spaces. Associated with this nonparametric estimation scheme is the issue of bandwidth selection and bias and variance assessment. This paper provides a unified approach to selecting a bandwidth and constructing confidence intervals in local maximum likelihood estimation. The approach is then applied to least squares nonparametric regression and to nonparametric logistic regression. Our experiences in these two settings show that the general idea outlined here is powerful and encouraging.