DATA-DRIVEN BANDWIDTH CHOICE FOR DENSITY-ESTIMATION BASED ON DEPENDENT DATA

被引：121
|
作者：
HART, JD [1 ]
VIEU, P [1 ]
机构：
[1] UNIV TOULOUSE 3,STAT PROBABILITES LAB,CNRS,UA 745,F-31062 TOULOUSE,FRANCE
来源：
ANNALS OF STATISTICS | 1990年 / 18卷 / 02期
关键词：
D O I：
10.1214/aos/1176347630
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：873 / 890
页数：18
相关论文
共 50 条
  • [1] LOCAL DATA-DRIVEN BANDWIDTH CHOICE FOR DENSITY-ESTIMATION
    MIELNICZUK, J
    SARDA, P
    VIEU, P
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 1989, 23 (01) : 53 - 69
  • [2] REMARK CONCERNING DATA-DEPENDENT BANDWIDTH CHOICE IN DENSITY-ESTIMATION
    MIELNICZUK, J
    [J]. STATISTICS & PROBABILITY LETTERS, 1990, 9 (01) : 27 - 33
  • [3] On bandwidth choice for density estimation with dependent data
    Hall, P
    Lahiri, SN
    Truong, YK
    [J]. ANNALS OF STATISTICS, 1995, 23 (06): : 2241 - 2263
  • [4] BOOTSTRAP CHOICE OF BANDWIDTH FOR DENSITY-ESTIMATION
    FARAWAY, JJ
    JHUN, MS
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1990, 85 (412) : 1119 - 1122
  • [5] A DATA DEPENDENT APPROACH TO DENSITY-ESTIMATION
    BURMAN, P
    [J]. ZEITSCHRIFT FUR WAHRSCHEINLICHKEITSTHEORIE UND VERWANDTE GEBIETE, 1985, 69 (04): : 609 - 628
  • [6] ON OPTIMAL DATA-BASED BANDWIDTH SELECTION IN KERNEL DENSITY-ESTIMATION
    HALL, P
    SHEATHER, SJ
    JONES, MC
    MARRON, JS
    [J]. BIOMETRIKA, 1991, 78 (02) : 263 - 269
  • [7] A RELIABLE DATA-BASED BANDWIDTH SELECTION METHOD FOR KERNEL DENSITY-ESTIMATION
    SHEATHER, SJ
    JONES, MC
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-METHODOLOGICAL, 1991, 53 (03): : 683 - 690
  • [8] On bandwidth choice for spatial data density estimation
    Jiang, Zhenyu
    Ling, Nengxiang
    Lu, Zudi
    Tjostheim, Dag
    Zhang, Qiang
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2020, 82 (03) : 817 - 840
  • [9] Data-driven deep density estimation
    Puchert, Patrik
    Hermosilla, Pedro
    Ritschel, Tobias
    Ropinski, Timo
    [J]. NEURAL COMPUTING & APPLICATIONS, 2021, 33 (23): : 16773 - 16807
  • [10] Data-driven deep density estimation
    Patrik Puchert
    Pedro Hermosilla
    Tobias Ritschel
    Timo Ropinski
    [J]. Neural Computing and Applications, 2021, 33 : 16773 - 16807
12345