用核方法对区间数据进行非参数推理

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2023-01-03 DOI:10.1080/10485252.2022.2160980

Hoyoung Park, J. Loh, Woncheol Jang

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引用次数: 0

摘要

在大数据时代，符号数据越来越受欢迎。本文研究了区间值数据的密度估计和回归，区间值数据是天文学和官方统计中常见的一种特殊类型的符号数据。我们提出了具有自适应带宽的核估计器来考虑每个区间的可变性。具体来说，我们推导了密度估计的交叉验证带宽选择器，并扩展了用于区间数据回归的Nadaraya-Watson估计器。通过广泛的仿真研究和实际数据分析，我们评估了所提出方法与现有核方法的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Nonparametric inference for interval data using kernel methods

ABSTRACT Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.

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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.