ADAPTIVE DISTRIBUTION-FREE REGRESSION METHODS AND THEIR APPLICATIONS

被引:17
|
作者
HOGG, RV [1 ]
RANDLES, RH [1 ]
机构
[1] UNIV IOWA,DEPT STATISTICS,IOWA CITY,IA 52242
关键词
D O I
10.2307/1268426
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
引用
收藏
页码:399 / 407
页数:9
相关论文
共 50 条
  • [1] ADAPTIVE DISTRIBUTION-FREE TESTS
    BUNING, H
    [J]. BIOMETRICS, 1984, 40 (01) : 261 - 262
  • [2] ADAPTIVE DISTRIBUTION-FREE INFERENCES
    RANDLES, RH
    HOGG, RV
    [J]. BIOMETRICS, 1972, 28 (04) : 1179 - 1179
  • [3] Distribution-Free Predictive Inference for Regression
    Lei, Jing
    G'Sell, Max
    Rinaldo, Alessandro
    Tibshirani, Ryan J.
    Wasserman, Larry
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2018, 113 (523) : 1094 - 1111
  • [4] Distribution-free properties of isotonic regression
    Soloff, Jake A.
    Guntuboyina, Adityanand
    Pitman, Jim
    [J]. ELECTRONIC JOURNAL OF STATISTICS, 2019, 13 (02): : 3243 - 3253
  • [5] A DISTRIBUTION-FREE TEST FOR REGRESSION PARAMETERS
    DANIELS, HE
    [J]. ANNALS OF MATHEMATICAL STATISTICS, 1954, 25 (03): : 499 - 513
  • [6] Distribution-free methods in statistics
    Conover, W. J.
    [J]. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2009, 1 (02): : 199 - 207
  • [7] DISTRIBUTION-FREE METHODS IN BIOASSAY
    BENNETT, BM
    [J]. BIOMETRICS, 1971, 27 (01) : 245 - &
  • [8] A class of adaptive distribution-free procedures
    Sun, S
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 1997, 59 (02) : 191 - 211
  • [9] Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
    Liu, Shang
    Cai, Zhongze
    Li, Xiaocheng
    [J]. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023), 2023,
  • [10] Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
    Angelopoulos, Anastasios N.
    Kohli, Amit
    Bates, Stephen
    Jordan, Michael I.
    Malik, Jitendra
    Alshaabi, Thayer
    Upadhyayula, Srigokul
    Romano, Yaniv
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162, 2022, : 717 - 730