
Abstract
Inference about a parameter of interest in presence of a nuisance parameter can be based on an integrated likelihood function. We analyze the behaviour of inferential quantities based on such a pseudo-likelihood in a two-index asymptotics framework, in which both sample size and dimension of the nuisance parameter may diverge to infinity. We show that the integrated likelihood, if chosen wisely, largely outperforms standard likelihood methods, such as the profile likelihood. These results are confirmed by simulation studies, in which comparisons with modified profile likelihood are also considered.
Item Type: | Paper |
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Keywords: | modified profile likelihood; non stationary autoregressive model; profile likelihood; profile score bias; target likelihood; two-index asymptotics. |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
Subjects: | 300 Social sciences > 310 Statistics |
URN: | urn:nbn:de:bvb:19-epub-19033-0 |
Language: | English |
Item ID: | 19033 |
Date Deposited: | 25. Mar 2014 08:28 |
Last Modified: | 04. Nov 2020 13:00 |