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
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
While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables(EV) model by the wavelet method. Under general condit...While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables(EV) model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.展开更多
Consider a semiparametric regression model Y_i=X_iβ+g(t_i)+e_i, 1 ≤ i ≤ n, where Y_i is censored on the right by another random variable C_i with known or unknown distribution G. The wavelet estimators of param...Consider a semiparametric regression model Y_i=X_iβ+g(t_i)+e_i, 1 ≤ i ≤ n, where Y_i is censored on the right by another random variable C_i with known or unknown distribution G. The wavelet estimators of parameter and nonparametric part are given by the wavelet smoothing and the synthetic data methods. Under general conditions, the asymptotic normality for the wavelet estimators and the convergence rates for the wavelet estimators of nonparametric components are investigated. A numerical example is given.展开更多
For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold ...For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.展开更多
The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which ...The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.展开更多
In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary α-mixing sequence. To simulate t...In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary α-mixing sequence. To simulate the distribution of estimator such that it is easy to perform statistical inference for the density function, a random weighted estimator of the density function is also constructed and investigated. Finite sample behavior of the estimator is investigated via simulations too.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
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
The inference for the parameters in a semiparametric regression model is studied by using the wavelet and the bootstrap methods. The bootstrap statistics are constructed by using Efron's resampling technique, and the...The inference for the parameters in a semiparametric regression model is studied by using the wavelet and the bootstrap methods. The bootstrap statistics are constructed by using Efron's resampling technique, and the strong uniform convergence of the bootstrap approximation is proved. Our results can be used to construct the large sample confidence intervals for the parameters of interest. A simulation study is conducted to evaluate the finite-sample performance of the bootstrap method and to compare it with the normal approximation-based method.展开更多
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(No.11471105,11471223)Scientific Research Item of Education Office,Hubei(No.D20172501)
文摘While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables(EV) model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.
基金Supported by the National Natural Science Foundation of China (11071022)the Key Project of Hubei Provincial Department of Education (D20092207)
文摘Consider a semiparametric regression model Y_i=X_iβ+g(t_i)+e_i, 1 ≤ i ≤ n, where Y_i is censored on the right by another random variable C_i with known or unknown distribution G. The wavelet estimators of parameter and nonparametric part are given by the wavelet smoothing and the synthetic data methods. Under general conditions, the asymptotic normality for the wavelet estimators and the convergence rates for the wavelet estimators of nonparametric components are investigated. A numerical example is given.
文摘For the nonparametric regression model Y-ni = g(x(ni)) + epsilon(ni)i = 1, ..., n, with regularly spaced nonrandom design, the authors study the behavior of the nonlinear wavelet estimator of g(x). When the threshold and truncation parameters are chosen by cross-validation on the everage squared error, strong consistency for the case of dyadic sample size and moment consistency for arbitrary sample size are established under some regular conditions.
基金Supported by the NNSF of China(70471057)Supported by the Natural Science Foundation of the Education Department of Shannxi Province(03JK065)
文摘The empirical Bayes test problem is considered for scale parameter of twoparameter exponential distribution under type-II censored data.By using wavelets estimation method,the EB test function is constructed,of which the asymptotic optimality and convergence rates are obtained.Finally,an example concerning the main result is given.
基金Supported by the National Natural Science Foundation of China (No.10871146)
文摘In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary α-mixing sequence. To simulate the distribution of estimator such that it is easy to perform statistical inference for the density function, a random weighted estimator of the density function is also constructed and investigated. Finite sample behavior of the estimator is investigated via simulations too.
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.
基金Supported by National Natural Science Foundation of China (Grant Nos. 10571008, 10871013)Beijing Natural Science Foundation (Grant No. 1072004)Ph.D. Program Foundation of Ministry of Education of China (Grant No. 20070005003)
文摘The inference for the parameters in a semiparametric regression model is studied by using the wavelet and the bootstrap methods. The bootstrap statistics are constructed by using Efron's resampling technique, and the strong uniform convergence of the bootstrap approximation is proved. Our results can be used to construct the large sample confidence intervals for the parameters of interest. A simulation study is conducted to evaluate the finite-sample performance of the bootstrap method and to compare it with the normal approximation-based method.