考虑半参数回归模型yi=xiβ+g(ti)+Vi(1≤i≤n), 其中(xi,ti)是已知的设计点, 斜率参数β是未知的, g(·)是未知函数, 误差Vi=sum from j=-∞ to ∞(cjei-j),sum from j=-∞ to ∞(|cj|<∞)并且ei是负相关的随机变量. 在适当的...考虑半参数回归模型yi=xiβ+g(ti)+Vi(1≤i≤n), 其中(xi,ti)是已知的设计点, 斜率参数β是未知的, g(·)是未知函数, 误差Vi=sum from j=-∞ to ∞(cjei-j),sum from j=-∞ to ∞(|cj|<∞)并且ei是负相关的随机变量. 在适当的条件下, 我们研究了β与g(·)小波估计量的强收敛速度. 结果显示g(·)的小波估计量达到最优收敛速度. 同时, 对β小波估计量也作了模拟研究.展开更多
We define a wavelet linear estimator for density derivative in Besov space based on a negatively associated stratified size-biased random sample. We provide two upper bounds of wavelet estimations on L^p （1 ≤ p 〈 ...We define a wavelet linear estimator for density derivative in Besov space based on a negatively associated stratified size-biased random sample. We provide two upper bounds of wavelet estimations on L^p （1 ≤ p 〈 ∞） risk.展开更多
This paper deals with the LP-consistency of wavelet estimators for a density function based on size-biased random samples. More precisely, we firstly show the LP-consistency of wavelet estimators for independent and i...This paper deals with the LP-consistency of wavelet estimators for a density function based on size-biased random samples. More precisely, we firstly show the LP-consistency of wavelet estimators for independent and identically distributed random vectors in Rd. Then a similar result is obtained for negatively associated samples under the additional assumptions d = 1 and the monotonicity of the weight function.展开更多
This paper considers the semiparametric regression model Yi = xiβ＋g（ti）＋ Vi （1 ≤ i≤ n）, where （xi, ti） are known design points, fl is an unknown slope parameter, g（.） is an unknown function, the correlate...This paper considers the semiparametric regression model Yi = xiβ＋g（ti）＋ Vi （1 ≤ i≤ n）, where （xi, ti） are known design points, fl is an unknown slope parameter, g（.） is an unknown function, the correlated errors Vi = ∑^∞j=-∞cjei-j with ∑^∞j=-∞｜cj｜ 〈 ∞, and ei are negatively associated random variables. Under appropriate conditions, the authors study the asymptotic normality for wavelet estimators ofβ and g（·）. A simulation study is undertaken to investigate finite sample behavior of the estimators.展开更多
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 cl...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.展开更多
The following heteroscedastic regression model Yi = g（xi） ＋σiei （1 ≤i ≤ n） is 2 considered, where it is assumed that σi^2 = f（ui）, the design points （xi,ui） are known and nonrandom, g and f are unknown f...The following heteroscedastic regression model Yi = g（xi） ＋σiei （1 ≤i ≤ n） is 2 considered, where it is assumed that σi^2 = f（ui）, the design points （xi,ui） are known and nonrandom, g and f are unknown functions. Under the unobservable disturbance ei form martingale differences, the asymptotic normality of wavelet estimators of g with f being known or unknown function is studied.展开更多
Consider the heteroscedastic regression model Yi = g（xi） ＋ σiei, 1 ≤ i ≤ n, where σi^2 = f（ui）, here （xi, ui） being fixed design points, g and f being unknown functions defined on [0, 1], ei being independe...Consider the heteroscedastic regression model Yi = g（xi） ＋ σiei, 1 ≤ i ≤ n, where σi^2 = f（ui）, here （xi, ui） being fixed design points, g and f being unknown functions defined on [0, 1], ei being independent random errors with mean zero. Assuming that Yi are censored randomly and the censored distribution function is known or unknown, we discuss the rates of strong uniformly convergence for wavelet estimators of g and f, respectively. Also, the asymptotic normality for the wavelet estimators of g is investigated.展开更多
基金supported by the National Natural Science Foundation of China(10871146)
文摘考虑半参数回归模型yi=xiβ+g(ti)+Vi(1≤i≤n), 其中(xi,ti)是已知的设计点, 斜率参数β是未知的, g(·)是未知函数, 误差Vi=sum from j=-∞ to ∞(cjei-j),sum from j=-∞ to ∞(|cj|<∞)并且ei是负相关的随机变量. 在适当的条件下, 我们研究了β与g(·)小波估计量的强收敛速度. 结果显示g(·)的小波估计量达到最优收敛速度. 同时, 对β小波估计量也作了模拟研究.
基金Acknowledgements The author would like to thank Professor Youming Liu, for his helpful guidance. This work was supported by the National Natural Science Foundation of China （Grant No. 11271038） and the Research Project of Baoji University of Arts and Sciences （ZK14060）.
文摘We define a wavelet linear estimator for density derivative in Besov space based on a negatively associated stratified size-biased random sample. We provide two upper bounds of wavelet estimations on L^p （1 ≤ p 〈 ∞） risk.
基金Supported by National Natural Science Foundation of China(Grant No.11271038)
文摘This paper deals with the LP-consistency of wavelet estimators for a density function based on size-biased random samples. More precisely, we firstly show the LP-consistency of wavelet estimators for independent and identically distributed random vectors in Rd. Then a similar result is obtained for negatively associated samples under the additional assumptions d = 1 and the monotonicity of the weight function.
基金supported by the National Natural Science Foundation of China under Grant No.10871146
文摘This paper considers the semiparametric regression model Yi = xiβ＋g（ti）＋ Vi （1 ≤ i≤ n）, where （xi, ti） are known design points, fl is an unknown slope parameter, g（.） is an unknown function, the correlated errors Vi = ∑^∞j=-∞cjei-j with ∑^∞j=-∞｜cj｜ 〈 ∞, and ei are negatively associated random variables. Under appropriate conditions, the authors study the asymptotic normality for wavelet estimators ofβ and g（·）. A simulation study is undertaken to investigate finite sample behavior of the estimators.
基金supported by the National Natural Science Foundation of China under Grant No.10871146the Grant MTM2008-03129 from the Spanish Ministry of Science and Innovation
文摘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.
基金Partially supported by the National Natural Science Foundation of China(10571136)
文摘The following heteroscedastic regression model Yi = g（xi） ＋σiei （1 ≤i ≤ n） is 2 considered, where it is assumed that σi^2 = f（ui）, the design points （xi,ui） are known and nonrandom, g and f are unknown functions. Under the unobservable disturbance ei form martingale differences, the asymptotic normality of wavelet estimators of g with f being known or unknown function is studied.
基金the National Natural Science Foundation of China(10571136)a Wonkwang University Grant in 2007
文摘Consider the heteroscedastic regression model Yi = g（xi） ＋ σiei, 1 ≤ i ≤ n, where σi^2 = f（ui）, here （xi, ui） being fixed design points, g and f being unknown functions defined on [0, 1], ei being independent random errors with mean zero. Assuming that Yi are censored randomly and the censored distribution function is known or unknown, we discuss the rates of strong uniformly convergence for wavelet estimators of g and f, respectively. Also, the asymptotic normality for the wavelet estimators of g is investigated.
