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2003•Journal of MathematicsRequires access

CONSISTENCY ESTIMATE FOR SEMIPARAMETRIC REGRESSION MODEL WITH NEGATIVELY ASSOCIATED ERROR'S STRUCTURE

Si‐Li Niu, Yamei Liu

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Abstract

For semiparametric regression model Y(x, t)=tb+g(x)+e(x), when the errors e(x) are negatively associated random errors, we study the strong consistency and rth mean (r 2) consistency for the estimators b and g(x) of b and g, respectively. The results of Hu on independent random errors are extended and the restrictions for weight function W(x) are weakened.

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What this paper is about

For semiparametric regression model Y(x, t)=tb+g(x)+e(x), when the errors e(x) are negatively associated random errors, we study the strong consistency and rth mean (r 2) consistency for the estimators b and g(x) of b and g, respectively. The results of Hu on independent random errors are extended and the restrictions for weight function W(x) are weakened.

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Available abstract

For semiparametric regression model Y(x, t)=tb+g(x)+e(x), when the errors e(x) are negatively associated random errors, we study the strong consistency and rth mean (r 2) consistency for the estimators b and g(x) of b and g, respectively. The results of Hu on independent random errors are extended and the restrictions for weight function W(x) are weakened.

Key concepts: Mathematics, Consistency (knowledge bases), Estimator, Strong consistency, Semiparametric regression, Statistics, Random error, Regression

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