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Robust tests based on dual divergence estimators and saddlepoint approximations
- Source :
- Journal of Multivariate Analysis, Journal of Multivariate Analysis, Elsevier, 2010, 101, pp.1143-1155
- Publisher :
- Elsevier Inc.
-
Abstract
- This paper is devoted to robust hypothesis testing based on saddlepoint approximations in the framework of general parametric models. As is known, two main problems can arise when using classical tests. First, the models are approximations of reality and slight deviations from them can lead to unreliable results when using classical tests based on these models. Then, even if a model is correctly chosen, the classical tests are based on first order asymptotic theory. This can lead to inaccurate p-values when the sample size is moderate or small. To overcome these problems, robust tests based on dual divergence estimators and saddlepoint approximations, with good performances in small samples, are proposed.
- Subjects :
- Statistics and Probability
Approximation theory
Numerical Analysis
010102 general mathematics
Estimator
[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Asymptotic theory (statistics)
M-estimators
01 natural sciences
010104 statistics & probability
Sample size determination
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
Divergences
Parametric model
Calculus
Applied mathematics
p-value
Robust testing
0101 mathematics
Statistics, Probability and Uncertainty
Divergence (statistics)
Saddlepoint approximations
ComputingMilieux_MISCELLANEOUS
Mathematics
Statistical hypothesis testing
Subjects
Details
- Language :
- English
- ISSN :
- 0047259X and 10957243
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- Journal of Multivariate Analysis
- Accession number :
- edsair.doi.dedup.....f51357342020d348403873c2bda6bae5
- Full Text :
- https://doi.org/10.1016/j.jmva.2009.11.001