Fuzzy-weighted bootstrap estimation in semi-parametric model

doi:10.1080/09720510.2003.10701092

機構典藏 > College of Business and Management > Graduate Institute & Department of Statistics > Journal Article > Item 987654321/18490

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/18490

Title:	Fuzzy-weighted bootstrap estimation in semi-parametric model
Authors:	Tsai, Tzong-ru;Wu, Shuo-jye
Contributors:	淡江大學統計學系
Keywords:	Bootstrap estimation;optimal fuzzy clustering analysis method;ordinary least squares estimation;percentile interval estimation;semi-parametric model
Date:	2003-06-14
Issue Date:	2009-09-01 16:09:12 (UTC+8)
Publisher:	TARU Publications
Abstract:	Many statisticians use a semi-parametric model to examine the relationship between explanatory and response variables. In practice, a data set often contains outliers and, hence the traditional estimation methods may not be appropriate. Wu et al. (1996) provided a fuzzy-weighted estimator to reduce the influence of outliers and they discussed its asymptotic properties. However, the test performance in small sample is not discussed. In this paper, a percentile interval estimation method based on fuzzy-weighted bootstrap samples is provided and we call the improved percentile interval estimation method. We show that the fuzzy-weighted bootstrap estimator and the fuzzy-weighted estimator have the same asymptotic properties. In addition, the proposed method can reduce the influence of outliers efficiently when we make inference about the scaled regression coefficient in small sample. Hence, we provide an alternative estimation method to make inference when sample size is small.
Relation:	Journal of statistics and management systems 6(3), p.443-461
DOI:	10.1080/09720510.2003.10701092
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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