ASYMPTOTIC NORMALITY OF WAVELET ESTIMATOR IN HETEROSCEDASTIC MODEL WITH α-MIXING ERRORS  

作　　者：Hanying LIANG 

机构地区：[1]Department of Mathematics, Tongji University, Shanghai 200092, China.

出　　处：《Journal of Systems Science & Complexity》2011年第4期725-737,共13页系统科学与复杂性学报（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grant No.10871146;the Grant MTM2008-03129 from the Spanish Ministry of Science and Innovation

摘　　要：Consider heteroscedastic regression model Yni= g（xni） ＋ σniεni （1 〈 i 〈 n）, where σ2ni= f（uni）, the design points （xni, uni） are known and nonrandom, g（.） and f（.） are unknown functions defined on closed interval [0, 1], and the random errors （εni, 1 ≤i≤ n） axe assumed to have the same distribution as （ξi, 1 ≤ i ≤ n）, which is a stationary and a-mixing time series with Eξi =0. Under appropriate conditions, we study asymptotic normality of wavelet estimators of g（.） and f（.）. Finite sample behavior of the estimators is investigated via simulations, too.

关 键 词：Α-MIXING asymptotic normality heteroscedastic regression model wavelet estimator. 

分 类 号：O212.1[理学—概率论与数理统计] TQ051.72[理学—数学]