Robust Semiparametric and Semi-Nonparametric Estimates of Inhomogeneous Experimental Data
| Autor: | A. E. Avdyushina, L. G. Shamanaeva, V. A. Simakhin |
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| Rok vydání: | 2021 |
| Předmět: |
параметрические оценки
010302 applied physics Estimation Data processing 010308 nuclear & particles physics Maximum likelihood Minor (linear algebra) Nonparametric statistics General Physics and Astronomy Experimental data 01 natural sciences Distribution (mathematics) неоднородные экспериментальные данные 0103 physical sciences Outlier Statistics::Methodology Applied mathematics статистическая обработка данных физические эксперименты робастные оценки Mathematics |
| Zdroj: | Russian physics journal. 2021. Vol. 64, № 2. P. 355-366 |
| ISSN: | 1573-9228 1064-8887 |
| DOI: | 10.1007/s11182-021-02336-z |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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