TY - JOUR
T1 - Maximum likelihood principle and model selection when the true model is unspecified
AU - Nishii, R.
N1 - Funding Information:
The author would like to express his thanks to Professor C. R. Rao of University of Pittsburgh and to Professor M. B. Rao of North Dakota State University for their helpful comments. This work was supported by Center for Multivariate Analysis, University of Pittsburgh.
Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
PY - 1988/11
Y1 - 1988/11
N2 - Suppose that independent observations come from an unspecified unknown distribution. Then we consider the maximum likelihood based on a specified parametric family which provides a good approximation of the true distribution. We examine the asymptotic properties of the maximum likelihood estimate and of the maximum likelihood. These results will be applied to the model selection problem.
AB - Suppose that independent observations come from an unspecified unknown distribution. Then we consider the maximum likelihood based on a specified parametric family which provides a good approximation of the true distribution. We examine the asymptotic properties of the maximum likelihood estimate and of the maximum likelihood. These results will be applied to the model selection problem.
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U2 - 10.1016/0047-259X(88)90137-6
DO - 10.1016/0047-259X(88)90137-6
M3 - Article
AN - SCOPUS:0001662579
VL - 27
SP - 392
EP - 403
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
SN - 0047-259X
IS - 2
ER -