In this article, a law of iterated logarithm for the maximum likelihood estimator in a random censoring model with incomplete information under certain regular conditions is obtained.
This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） prop...This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.展开更多
In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, ...In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.展开更多
Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobse...Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator for the asymptotic variance of βn(βn) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for,En, and obtain the convergest rates for En and the strong uniform convergent rates for gn(gn).展开更多
For a linear model, let the error sequence be i.i.d, with common unknown density f(x), and (x) be a nonparametric estimator of f(x) based on the residuals. In this paper, on the basis of [1], we establish the L_1-norm...For a linear model, let the error sequence be i.i.d, with common unknown density f(x), and (x) be a nonparametric estimator of f(x) based on the residuals. In this paper, on the basis of [1], we establish the L_1-norm consistency, asymptotic normality and law of iterated logarithm for (x) under general condition. These results bring the asymptotic theory for estimation of error distributions to completion.展开更多
This paper studies the strong and weak representation of M-estimates of multiple regression coefficients when the convexity condition is not assumed.The order of the remainder term,or its principal part,is accurate.Us...This paper studies the strong and weak representation of M-estimates of multiple regression coefficients when the convexity condition is not assumed.The order of the remainder term,or its principal part,is accurate.Using the result,we obtain the convergence rate,the LIL and Berry-Esseen type bounds of the M-estimate.展开更多
文摘In this article, a law of iterated logarithm for the maximum likelihood estimator in a random censoring model with incomplete information under certain regular conditions is obtained.
基金supported by the National Natural Science Funds for Distinguished Young Scholar (70825004)National Natural Science Foundation of China (NSFC) (10731010 and 10628104)+3 种基金the National Basic Research Program (2007CB814902)Creative Research Groups of China (10721101)Leading Academic Discipline Program, the 10th five year plan of 211 Project for Shanghai University of Finance and Economics211 Project for Shanghai University of Financeand Economics (the 3rd phase)
文摘This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.
文摘In this paper, we define the generalized linear models (GLM) based on the observed data with incomplete information and random censorship under the case that the regressors are stochastic. Under the given conditions, we obtain a law of iterated logarithm and a Chung type law of iterated logarithm for the maximum likelihood estimator (MLE) in the present model.
文摘Consider tile partial linear model Y=Xβ+ g(T) + e. Wilers Y is at risk of being censored from the right, g is an unknown smoothing function on [0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator for the asymptotic variance of βn(βn) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for,En, and obtain the convergest rates for En and the strong uniform convergent rates for gn(gn).
基金Project supported by the National Natural Science Foundation of China.
文摘For a linear model, let the error sequence be i.i.d, with common unknown density f(x), and (x) be a nonparametric estimator of f(x) based on the residuals. In this paper, on the basis of [1], we establish the L_1-norm consistency, asymptotic normality and law of iterated logarithm for (x) under general condition. These results bring the asymptotic theory for estimation of error distributions to completion.
基金Project supported by the National Natural Science Foundation of China
文摘This paper studies the strong and weak representation of M-estimates of multiple regression coefficients when the convexity condition is not assumed.The order of the remainder term,or its principal part,is accurate.Using the result,we obtain the convergence rate,the LIL and Berry-Esseen type bounds of the M-estimate.
