Asymptotic Normality for Wavelet Estimators in Heteroscedastic Semiparametric Model with Random Errors

被引：0
作者：
Liwang Ding
Ping Chen
Qiang Zhang
Yongming Li
机构：
[1] Nanjing University of Science and Technology,School of Science
[2] Guangxi University of Finance and Economics,School of Information and Statistics
[3] Shangrao Normal University,School of Mathematics and Computer Science
来源：
Journal of Systems Science and Complexity | 2020年 / 33卷
关键词：
Asymptotic normality; heteroscedastic semiparametric model; strong mixing; wavelet estimator;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
For the heteroscedastic regression model Yi = xiβ + g(ti) + σiei, 1 ≤ i ≤ n, where σi2 = f (ui), the design points (xi, ti, ui) are known and nonrandom, g(·) and f(·) are defined on the closed interval [0,1]. When f(·) is known, we investigate the asymptotic normality for wavelet estimators of β and g(·) under {ei, 1 ≤ i ≤ n} is a sequence of identically distributed a-mixing errors; when f(·) is unknown, the asymptotic normality for wavelet estimators of β, g(·) and f(·) are established under independent errors. A simulation study is provided to illustrate the feasibility of the theoretical result that the authors derived.
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页码：1212 / 1243
页数：31
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