Fast and Robust Bootstrap for Multivariate Inference: The R Package FRB

被引:0
|
作者
Van Aelst, Stefan [1 ]
Willems, Gert [1 ]
机构
[1] Univ Ghent, Dept Appl Math & Comp Sci, B-9000 Ghent, Belgium
来源
JOURNAL OF STATISTICAL SOFTWARE | 2013年 / 53卷 / 03期
关键词
robustness; multivariate regression; principal components analysis; Hotelling tests; outliers; GENERALIZED S-ESTIMATORS; REGRESSION;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We present the FRB package for R, which implements the fast and robust bootstrap. This method constitutes an alternative to ordinary bootstrap or asymptotic inference procedures when using robust estimators such as S-, MM- or GS-estimators. The package considers three multivariate settings: principal components analysis, Hotelling tests and multivariate regression. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap. In this paper we give some background on the method, discuss the implementation and provide various examples.
引用
收藏
页码:1 / 32
页数:32
相关论文
共 50 条
  • [31] BootES: An R package for bootstrap confidence intervals on effect sizes
    Kirby, Kris N.
    Gerlanc, Daniel
    [J]. BEHAVIOR RESEARCH METHODS, 2013, 45 (04) : 905 - 927
  • [32] Maximum Entropy Bootstrap for Time Series: The meboot R Package
    Vinod, Hrishikesh D.
    Lopez-de-Lacalle, Javier
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2009, 29 (05): : 1 - 19
  • [33] BootES: An R package for bootstrap confidence intervals on effect sizes
    Kris N. Kirby
    Daniel Gerlanc
    [J]. Behavior Research Methods, 2013, 45 : 905 - 927
  • [34] Robust likelihood inference for multivariate correlated count data
    Tsung-Shan Tsou
    [J]. Computational Statistics, 2016, 31 : 845 - 857
  • [35] Robust likelihood inference for multivariate correlated count data
    Tsou, Tsung-Shan
    [J]. COMPUTATIONAL STATISTICS, 2016, 31 (03) : 845 - 857
  • [36] ROBUST, SPARSE AND SCALABLE INFERENCE USING BOOTSTRAP AND VARIABLE SELECTION FUSION
    Mozafari-Majd, Emadaldin
    Koivunen, Visa
    [J]. 2019 IEEE 8TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2019), 2019, : 271 - 275
  • [37] xcore: an R package for inference of gene expression regulators
    Maciej Migdał
    Takahiro Arakawa
    Satoshi Takizawa
    Masaaki Furuno
    Harukazu Suzuki
    Erik Arner
    Cecilia Lanny Winata
    Bogumił Kaczkowski
    [J]. BMC Bioinformatics, 24
  • [38] xcore: an R package for inference of gene expression regulators
    Migdal, Maciej
    Arakawa, Takahiro
    Takizawa, Satoshi
    Furuno, Masaaki
    Suzuki, Harukazu
    Arner, Erik
    Winata, Cecilia Lanny
    Kaczkowski, Bogumil
    [J]. BMC BIOINFORMATICS, 2023, 24 (01)
  • [39] BayesBD: An R Package for Bayesian Inference on Image Boundaries
    Syring, Nicholas
    Li, Meng
    [J]. R JOURNAL, 2017, 9 (02): : 149 - 162
  • [40] Fast Bayesian inference for modeling multivariate crash counts
    Serhiyenko, Volodymyr
    Mamun, Sha A.
    Ivan, John N.
    Ravishanker, Nalini
    [J]. ANALYTIC METHODS IN ACCIDENT RESEARCH, 2016, 9 : 44 - 53