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线性模型的一步 L 估计量AI 解读

We propose and investigate the asymptotic properties of a class of estimators of the regression parameter in the general linear model. The estimators depend on a preliminary estimate of the regression parameter and the residuals based on it. For the location model, the estimators are linear combinations of the order statistics and the robustness and efficiency properties of this class of estimators carry over to the general linear model. The estimators settle the doubts…

线性模型中的截尾均值AI 解读

For the general linear model with independent errors, we propose and examine the large sample properties of an estimator of the regression parameter. In the location model, the estimator has the same properties as the trimmed mean and the robustness and efficiency properties of the trimmed mean carry over to the general model. The estimator depends on a preliminary estimate of the regression parameter and the residuals based on it. The properties of the adaptive estimator…

基于回归残差的稳健尺度估计量的 Bahadur 表示AI 解读

We investigate the asymptotic behaviour of the median deviation and the semi-interquartile range based on the residuals from a linear regression model by deriving weak asymptotic representations for the estimators. These representations may be used to obtain a variety of central limit theorems and yield conditions under which the median deviation and the semi-interquartile range are asymptotically equivalent. The results justify the use of the estimators as concommitant…

相依回归量下的稳健回归估计：函数型最小二乘方法AI 解读

In the case of regression models, one robust estimation procedure which has recently emerged is that of functional least squares. The procedure is based on the use of characteristic functions for which the tail behavior is relected by the behavior of these functions at the origin. Its attraction is that it is applicable to situations where the distribution of the disturbances may be long-tailed and/or asymmetric. This paper extends this theory to include a large class of…

正则变动参数的自适应估计AI 解读

The problem of estimating shape and scale parameters for a distribution with regularly varying tails is related to that of nonparametrically estimating a density at a fixed point, in that optimal construction of the estimators depends substantially upon unknown features of the distribution. We show how to overcome this problem by using adaptive methods. Our main results hold very generally, for a large class of adaptive estimators. Later we consider specific versions of…

摘要暂缺

正则变尾参数估计的最优可达收敛速率AI 解读

We derive lower bounds to rates of convergence for estimators of shape and scale parameters in distributions with regularly varying tails. We exhibit simple estimators which attain these rates.

《基于经验特征函数的正态性检验》的模拟结果更正AI 解读

The last four rows of Table 1 in this paper are in error, due to a mis-set parameter in the simulations. New, extensive simulations show that the empiric characteristic function method, column A, tends to be superior against contaminated normal CN(α,σ2) alternatives when σ2 is small, but tends to be inferior when σ2 is large. We give below corrected versions of the 4th last and 3rd last rows of Table 1, and two new rows which indicate behaviour for smaller values of σ2. The…

基于经验特征函数的正态性检验AI 解读

We present a simple test for normality against the alternative that the underlying distribution is long tailed. The test is based on the behaviour of the empirical characteristic function in the neighbourhood of the origin, and is very competitive with several well-known tests for normality.

基于经验特征函数的正态性检验AI 解读

We present a simple test for normality against the alternative that the underlying distribution is long tailed. The test is based on the behaviour of the empirical characteristic function in the neighbourhood of the origin, and is very competitive with several well-known tests for normality.