Robust semiparametric and semi-nonparametric estimates of inhomogeneous experimental data V. A. Simakhin, L. G. Shamanaeva, A. E. Avdyushina
Material type:
ArticleContent type: Текст Media type: электронный Subject(s): физические эксперименты | статистическая обработка данных | неоднородные экспериментальные данные | параметрические оценки | робастные оценкиGenre/Form: статьи в журналах Online resources: Click here to access online
In:
Russian physics journal Vol. 64, № 2. P. 355-366Abstract: A weighted maximum likelihood method (WMLM) of robust estimation of experimental data with outliers is proposed in this work. The method allows effective robust asymptotically unbiased estimates to be obtained under conditions of aprioristic uncertainty. Based on the WMLM, adaptive robust algorithms have been synthesized for solving semiparametric and semi-nonparametric problems of heterogeneous data processing. It is shown that for heterogeneous data samples, these estimates converge to the maximum likelihood estimates for each distribution from the Tukey supermodel not only in the presence of major, but also minor asymmetric and symmetric outliers.
Библиогр.: 26 назв.
A weighted maximum likelihood method (WMLM) of robust estimation of experimental data with outliers is proposed in this work. The method allows effective robust asymptotically unbiased estimates to be obtained under conditions of aprioristic uncertainty. Based on the WMLM, adaptive robust algorithms have been synthesized for solving semiparametric and semi-nonparametric problems of heterogeneous data processing. It is shown that for heterogeneous data samples, these estimates converge to the maximum likelihood estimates for each distribution from the Tukey supermodel not only in the presence of major, but also minor asymmetric and symmetric outliers.
There are no comments on this title.