On consistency of the weighted least squares estimators in a semiparametric regression model

Xuejun Wang (), Xin Deng and Shuhe Hu
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Xuejun Wang: Anhui University
Xin Deng: Anhui University
Shuhe Hu: Anhui University

Metrika: International Journal for Theoretical and Applied Statistics, 2018, vol. 81, issue 7, No 3, 797-820

Abstract: Abstract This paper is concerned with the semiparametric regression model $$y_i=x_i\beta +g(t_i)+\sigma _ie_i,~~i=1,2,\ldots ,n,$$ y i = x i β + g ( t i ) + σ i e i , i = 1 , 2 , … , n , where $$\sigma _i^2=f(u_i)$$ σ i 2 = f ( u i ) , $$(x_i,t_i,u_i)$$ ( x i , t i , u i ) are known fixed design points, $$\beta $$ β is an unknown parameter to be estimated, $$g(\cdot )$$ g ( · ) and $$f(\cdot )$$ f ( · ) are unknown functions, random errors $$e_i$$ e i are widely orthant dependent random variables. The p-th ( $$p>0$$ p > 0 ) mean consistency and strong consistency for least squares estimators and weighted least squares estimators of $$\beta $$ β and g under some more mild conditions are investigated. A simulation study is also undertaken to assess the finite sample performance of the results that we established. The results obtained in the paper generalize and improve some corresponding ones of negatively associated random variables.

Keywords: Semiparametric regression model; Widely orthant dependent random error; Least squares estimator; Consistency; 62F12; 62G20 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00184-018-0659-y

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