The minimum L2 distance estimator for Poisson mixture models

Autor:	Ian R. Harris, Shuyi Shen
Rok vydání:	2011
Předmět:	Statistics and Probability Applied Mathematics Robust statistics Estimator Trimmed estimator Efficient estimator Minimum-variance unbiased estimator Bias of an estimator Consistent estimator Statistics Applied mathematics Statistics Probability and Uncertainty Invariant estimator Mathematics
Zdroj:	Journal of Statistical Planning and Inference. 141:1088-1101
ISSN:	0378-3758
DOI:	10.1016/j.jspi.2010.09.002
Popis:	A robust estimator is developed for Poisson mixture models with a known number of components. The proposed estimator minimizes the L2 distance between a sample of data and the model. When the component distributions are completely known, the estimators for the mixing proportions are in closed form. When the parameters for the component Poisson distributions are unknown, numerical methods are needed to calculate the estimators. Compared to the minimum Hellinger distance estimator, the minimum L2 estimator can be less robust to extreme outliers, and often more robust to moderate outliers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::eccac6842fdd5d61dadb13849ef13217 https://doi.org/10.1016/j.jspi.2010.09.002 Zobrazit plný text záznamu Full Text from ScienceDirect