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The Consistency for the Estimators of Semiparametric Regression Model with Dependent Samples
Acta Mathematicae Applicatae Sinica, English Series ( IF 0.9 ) Pub Date : 2021-04-24 , DOI: 10.1007/s10255-021-1008-x
Yi Wu , Xue-jun Wang , Ling Chen , Kun Jiang

For the semiparametric regression model: \(Y^{(j)}(x_{in},t_{in})=t_{in}\beta+g(x_{in})+e^{(j)}(x_{in})\), 1 ≤ jk, 1≤ in, where tin ∈ ℝ and xin ∈ ℝp are known to be nonrandom, g is an unknown continuous function on a compact set A in ℝp, ej(xin) are m-extended negatively dependent random errors with mean zero, Y(j)(xin, tin) represent the j-th response variables which are observable at points xin, tin. In this paper, we study the strong consistency, complete consistency and r-th (r > 1) mean consistency for the estimators βk,n and gk,n of β and g, respectively. The results obtained in this paper markedly improve and extend the corresponding ones for independent random variables, negatively associated random variables and other mixing random variables. Moreover, we carry out a numerical simulation for our main results.



中文翻译:

相依样本的半参数回归模型估计量的一致性

对于半参数回归模型:\(Y ^ {(j)}(x_ {in},t_ {in})= t_ {in} \ beta + g(x_ {in})+ e ^ {(j)}( X_ {IN})\),1≤ Ĵķ,1≤ñ,其中∈ℝ和X∈ℝ p是已知的非随机,是一个紧凑的集合的未知连续函数在ℝ pe jx in)是m扩展的负相关的随机误差,均值为零,Y jx int in)表示在点x int in上可观察到的第j个响应变量。在本文中,我们研究了强一致性,完全的一致性和- [R个([R > 1)的估计量平均一致性β K,NK,Nβ分别。本文获得的结果显着改善并扩展了独立随机变量,负相关随机变量和其他混合随机变量的对应变量。此外,我们对主要结果进行了数值模拟。

更新日期:2021-04-24
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