[图书][B] Robust statistics: theory and methods (with R)
A new edition of this popular text on robust statistics, thoroughly updated to include new and
improved methods and focus on implementation of methodology using the increasingly …
improved methods and focus on implementation of methodology using the increasingly …
Robust rank correlation based screening
Robust rank correlation based screening Page 1 The Annals of Statistics 2012, Vol. 40, No. 3,
1846–1877 DOI: 10.1214/12-AOS1024 © Institute of Mathematical Statistics, 2012 ROBUST …
1846–1877 DOI: 10.1214/12-AOS1024 © Institute of Mathematical Statistics, 2012 ROBUST …
Statistical consistency and asymptotic normality for high-dimensional robust -estimators
PL Loh - 2017 - projecteuclid.org
Statistical consistency and asymptotic normality for high-dimensional robust M-estimators Page
1 The Annals of Statistics 2017, Vol. 45, No. 2, 866–896 DOI: 10.1214/16-AOS1471 © Institute of …
1 The Annals of Statistics 2017, Vol. 45, No. 2, 866–896 DOI: 10.1214/16-AOS1471 © Institute of …
Sparse least trimmed squares regression for analyzing high-dimensional large data sets
Sparse model estimation is a topic of high importance in modern data analysis due to the
increasing availability of data sets with a large number of variables. Another common …
increasing availability of data sets with a large number of variables. Another common …
Model selection via Bayesian information criterion for quantile regression models
ER Lee, H Noh, BU Park - Journal of the American Statistical …, 2014 - Taylor & Francis
Bayesian information criterion (BIC) is known to identify the true model consistently as long
as the predictor dimension is finite. Recently, its moderate modifications have been shown to …
as the predictor dimension is finite. Recently, its moderate modifications have been shown to …
Robust and sparse estimators for linear regression models
Penalized regression estimators are popular tools for the analysis of sparse and high-
dimensional models. However, penalized regression estimators defined using an …
dimensional models. However, penalized regression estimators defined using an …
Robust statistics: a selective overview and new directions
M Avella Medina, E Ronchetti - Wiley Interdisciplinary Reviews …, 2015 - Wiley Online Library
Classical statistics relies largely on parametric models. Typically, assumptions are made on
the structural and the stochastic parts of the model and optimal procedures are derived …
the structural and the stochastic parts of the model and optimal procedures are derived …
Statistical inference for nonignorable missing-data problems: a selective review
N Tang, Y Ju - Statistical Theory and Related Fields, 2018 - Taylor & Francis
Nonignorable missing data are frequently encountered in various settings, such as
economics, sociology and biomedicine. We review statistical inference for nonignorable …
economics, sociology and biomedicine. We review statistical inference for nonignorable …
Robust lasso regression using Tukey's biweight criterion
The adaptive lasso is a method for performing simultaneous parameter estimation and
variable selection. The adaptive weights used in its penalty term mean that the adaptive …
variable selection. The adaptive weights used in its penalty term mean that the adaptive …
Robust and consistent variable selection in high-dimensional generalized linear models
M Avella-Medina, E Ronchetti - Biometrika, 2018 - academic.oup.com
Generalized linear models are popular for modelling a large variety of data. We consider
variable selection through penalized methods by focusing on resistance issues in the …
variable selection through penalized methods by focusing on resistance issues in the …