Premium
Goodness of Fit via Non‐parametric Likelihood Ratios
Author(s) -
Claeskens Gerda,
Hjort Nils Lid
Publication year - 2004
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2004.00403.x
Subject(s) - mathematics , akaike information criterion , bayesian information criterion , goodness of fit , statistics , parametric statistics , likelihood ratio test , lemma (botany) , sample size determination , information criteria , model selection , statistical hypothesis testing , ecology , poaceae , biology
. To test if a density f is equal to a specified f 0 , one knows by the Neyman–Pearson lemma the form of the optimal test at a specified alternative f 1 . Any non‐parametric density estimation scheme allows an estimate of f . This leads to estimated likelihood ratios. Properties are studied of tests which for the density estimation ingredient use log‐linear expansions. Such expansions are either coupled with subset selectors like the Akaike information criterion and the Bayesian information criterion regimes, or use order growing with sample size. Our tests are generalized to testing the adequacy of general parametric models, and to work also in higher dimensions. The tests are related to, but are different from, the ‘smooth tests’ that go back to Neyman [Skandinavisk Aktuarietidsskrift 20(1937) 149] and that have been studied extensively in recent literature. Our tests are large‐sample equivalent to such smooth tests under local alternative conditions, but different from the smooth tests and often better under non‐local conditions.