泛函期望回归局部线性估计量的渐近正态性

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Journal of Multivariate Analysis Pub Date : 2023-12-05 DOI:10.1016/j.jmva.2023.105281

Ouahiba Litimein , Ali Laksaci , Larbi Ait-Hennani , Boubaker Mechab , Mustapha Rachdi

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引用次数: 0

摘要

我们关注期望函数回归的非参数估计。更精确地说，我们用局部线性平滑方法建立了一个条件期望的估计量。然后建立了构造的估计量的渐近分布。建立这个结果需要条件谓词的Bahadur表示。后者是在一定的标准条件下获得的，这些标准条件涵盖了数据的功能方面以及模型的非参数特性。这一结果在非参数函数统计中的实际影响已经用人工数据进行了讨论和强调。

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Asymptotic normality of the local linear estimator of the functional expectile regression

We are concerned with the nonparametric estimation of the expectile functional regression. More precisely, we build an estimator, by the local linear smoothing approach, of the conditional expectile. Then we establish the asymptotic distribution of the constructed estimator. Establishing this result requires the Bahadur representation of the conditional expectile. The latter is obtained under certain standard conditions which cover the functional aspect of the data as well as the nonparametric characteristic of the model. The real impact of this result in nonparametric functional statistics has been discussed and highlighted using artificial data.

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来源期刊

Journal of Multivariate Analysis 数学-统计学与概率论

CiteScore

2.40

自引率

25.00%

发文量

108

审稿时长

74 days

期刊介绍： Founded in 1971, the Journal of Multivariate Analysis (JMVA) is the central venue for the publication of new, relevant methodology and particularly innovative applications pertaining to the analysis and interpretation of multidimensional data. The journal welcomes contributions to all aspects of multivariate data analysis and modeling, including cluster analysis, discriminant analysis, factor analysis, and multidimensional continuous or discrete distribution theory. Topics of current interest include, but are not limited to, inferential aspects of Copula modeling Functional data analysis Graphical modeling High-dimensional data analysis Image analysis Multivariate extreme-value theory Sparse modeling Spatial statistics.