The authors establish the approximations to the distribution of M-estimates in a linear model by the bootstrap and the linear representation of bootstrap M-estimation, and prove that the approximation is valid in probability 1. A simulation is made to show the effects of bootstrap approximation, randomly weighted approximation and normal approximation
Includes bibliographical references (pages 43-46)A weighted bootstrap method is considered to approx...
AbstractM-estimation is a widely used technique for statistical inference. In this paper, we study p...
Bootstrap approximations to the sampling distribution can be seen as generalized statistics taking v...
We consider the M-estimation of regression parameters in the linear model by minimizing the sum of c...
The limiting distribution of M-estimators of the regression parameter in linear models is derived un...
Consider M-estimation in a semiparametric model that is charac-terized by a Euclidean parameter of i...
The sampling distribution of several commonly occurring statistics are known to be closer to the cor...
AbstractLet (X, Y) be a random vector in the plane and denote by m(x) = E(Y|X = x) the corresponding...
A bootstrap method for generating confidence intervals in linear models is suggested. The method is ...
We consider the weighted bootstrap approximation of the distribution of a class of M-estimators of t...
Abstract: The limiting distribution forM-estimates in a regression or autoregression model with heav...
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting...
This paper establishes the asymptotic validity for the moving block bootstrap as an approximation to...
Includes bibliographical references (pages 43-46)A weighted bootstrap method is considered to approx...
AbstractM-estimation is a widely used technique for statistical inference. In this paper, we study p...
Bootstrap approximations to the sampling distribution can be seen as generalized statistics taking v...
We consider the M-estimation of regression parameters in the linear model by minimizing the sum of c...
The limiting distribution of M-estimators of the regression parameter in linear models is derived un...
Consider M-estimation in a semiparametric model that is charac-terized by a Euclidean parameter of i...
The sampling distribution of several commonly occurring statistics are known to be closer to the cor...
AbstractLet (X, Y) be a random vector in the plane and denote by m(x) = E(Y|X = x) the corresponding...
A bootstrap method for generating confidence intervals in linear models is suggested. The method is ...
We consider the weighted bootstrap approximation of the distribution of a class of M-estimators of t...
Abstract: The limiting distribution forM-estimates in a regression or autoregression model with heav...
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting...
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resam...
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting...
This paper establishes the asymptotic validity for the moving block bootstrap as an approximation to...
Includes bibliographical references (pages 43-46)A weighted bootstrap method is considered to approx...
AbstractM-estimation is a widely used technique for statistical inference. In this paper, we study p...
Bootstrap approximations to the sampling distribution can be seen as generalized statistics taking v...
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