Inference for nonsmooth regression curves and surfaces using kernel-based methods

被引:4
|
作者
Gijbels, I [1 ]
机构
[1] Univ Catholique Louvain, Inst Stat, B-1348 Louvain, Belgium
关键词
D O I
10.1016/B978-044451378-6/50013-2
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In this paper we review kernel-based methods for detecting discontinuities in an otherwise smooth regression function or surface. In case of a possible discontinuous curve the interest might be in detecting the discontinuities, their jump sizes and finally to estimate the discontinuous curve. Alternatively, one might be uniquely interested in estimating directly the discontinuous curve preserving the jumps. A brief discussion on available kernel-based methods for testing for a continuous versus a discontinuous regression function, and for detecting discontinuities in regression surfaces is also provided.
引用
下载
收藏
页码:183 / 201
页数:19
相关论文
共 50 条
  • [41] Kernel-Based Methods for Bandit Convex Optimization
    Bubeck, Sebastien
    Lee, Yin Tat
    Eldan, Ronen
    STOC'17: PROCEEDINGS OF THE 49TH ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING, 2017, : 72 - 85
  • [42] An Adaptive Kernel-Based Bayesian Inference Technique for Failure Classification
    Reimann, Johan
    Kacprzynski, Greg
    2010 IEEE AEROSPACE CONFERENCE PROCEEDINGS, 2010,
  • [43] Mobility using First and Second Derivatives for Kernel-based Regression in Wireless Sensor Networks
    Ghadban, Nisrine
    Honeine, Paul
    Mourad-Chehade, Farah
    Francis, Clovis
    Farah, Joumana
    21ST INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP 2014), 2014, : 203 - 206
  • [44] Kernel-based sparse regression with the correntropy-induced loss
    Chen, Hong
    Wang, Yulong
    APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2018, 44 (01) : 144 - 164
  • [45] UNCERTAINTY BOUNDS FOR KERNEL-BASED REGRESSION: A BAYESIAN SPS APPROACH
    Care, Algo
    Pillonetto, Gianluigi
    Campi, Marco C.
    2018 IEEE 28TH INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2018,
  • [46] Kernel-based regression of drift and diffusion coefficients of stochastic processes
    Lamouroux, David
    Lehnertz, Klaus
    PHYSICS LETTERS A, 2009, 373 (39) : 3507 - 3512
  • [47] The Kernel-Based Regression for Seismic Attenuation Estimation on Wasserstein Space
    Zhang, Mingke
    Gao, Jinghuai
    Wang, Zhiguo
    Yang, Yang
    Liu, Naihao
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62
  • [48] Consistency and robustness of kernel-based regression in convex risk minimization
    Christmann, Andreas
    Steinwart, Ingo
    BERNOULLI, 2007, 13 (03) : 799 - 819
  • [49] Improving kernel-based nonparametric regression for circular–linear data
    Yasuhito Tsuruta
    Masahiko Sagae
    Japanese Journal of Statistics and Data Science, 2022, 5 : 111 - 131
  • [50] Cutting Plane Method for Continuously Constrained Kernel-Based Regression
    Sun, Zhe
    Zhang, Zengke
    Wang, Huangang
    Jiang, Min
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (02): : 238 - 247