Back to Search
Start Over
Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy
- Source :
- Computational Statistics & Data Analysis. 54:531-545
- Publication Year :
- 2010
- Publisher :
- Elsevier BV, 2010.
-
Abstract
- The likelihood approach based on the empirical distribution functions is a well-accepted statistical tool for testing. However, the proof schemes of the Neyman-Pearson type lemmas induce consideration of density-based likelihood ratios to obtain powerful test statistics. In this article, we introduce the distribution-free density-based likelihood technique, applied to test for goodness-of-fit. We focus on tests for normality and uniformity, which are common tasks in applied studies. The well-known goodness-of-fit tests based on sample entropy are shown to be a product of the proposed empirical likelihood (EL) methodology. Although the efficiency of test statistics based on classes of entropy estimators has been widely addressed in the statistical literature, estimation of the sample entropy has been not invariantly defined, and hence this estimation produces tests that are difficult to be applied to real data studies. The proposed EL approach defines clear forms of the entropy-based tests. Monte Carlo simulation results confirm the preference of the proposed method from a power perspective. Real data examples study the proposed approach in practice.
- Subjects :
- Statistics and Probability
Applied Mathematics
Maximum entropy spectral estimation
Likelihood principle
Computational Mathematics
Normality test
Empirical likelihood
Computational Theory and Mathematics
Goodness of fit
Likelihood-ratio test
Statistics
Entropy (information theory)
Likelihood function
Mathematics
Subjects
Details
- ISSN :
- 01679473
- Volume :
- 54
- Database :
- OpenAIRE
- Journal :
- Computational Statistics & Data Analysis
- Accession number :
- edsair.doi...........29fef7e4fc13aa1ae78ecd700826130a