Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function-Reference-Cited by-同舟云学术

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Goodness-of-fit tests for semiparametric and parametric hypotheses based on the probability weighted empirical characteristic function

Published:2016-03-23 Issue:4 Volume:57 Page:957-976
ISSN:0932-5026
Container-title:Statistical Papers
language:en
Short-container-title:Stat Papers

Author:

Meintanis Simos G.,Allison James,Santana Leonard

Funder

Special Account for Research Grants (ELKE)

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s00362-016-0760-0.pdf

Reference34 articles.

1. Azzalini A, Dalla Valle A (1996) The multivariate skew-normal distribution. Biometrika 83:715–726

2. Baringhaus L (1996) Fibonacci numbers, Lucas numbers and integrals of certain Gaussian processes. Proc Am Math Soc 124:3875–3884

3. Baringhaus L, Henze N (1988) A consistent test for multivariate normality based on the empirical characteristic function. Metrika 35:339–348

4. Bates D (2011) Computational methods for mixed models. URL http://cran.r-project.org/web/packages/lme4/vignettes/Theory.pdf

5. Caeiro F, Gomes MI (2011) Semi-parametric tail inference through probability-weighted moments. J Stat Plan Inference 141:935–950

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