Challenges of big data analysis
Big Data bring new opportunities to modern society and challenges to data scientists. On the
one hand, Big Data hold great promises for discovering subtle population patterns and …
one hand, Big Data hold great promises for discovering subtle population patterns and …
Sparse high-dimensional models in economics
This article reviews the literature on sparse high-dimensional models and discusses some
applications in economics and finance. Recent developments in theory, methods, and …
applications in economics and finance. Recent developments in theory, methods, and …
Feature screening via distance correlation learning
This article is concerned with screening features in ultrahigh-dimensional data analysis,
which has become increasingly important in diverse scientific fields. We develop a sure …
which has become increasingly important in diverse scientific fields. We develop a sure …
[图书][B] Statistical foundations of data science
Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …
statistical models, contemporary statistical machine learning techniques and algorithms …
Nonparametric independence screening in sparse ultra-high-dimensional additive models
A variable screening procedure via correlation learning was proposed by Fan and Lv (2008)
to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true …
to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true …
[图书][B] Handbook of survival analysis
JP Klein, HC Van Houwelingen, JG Ibrahim… - 2014 - api.taylorfrancis.com
This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …
data analysis. This area of statistics deals with time-to-event data which is complicated not …
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Variance estimation using refitted cross-validation in ultrahigh dimensional regression
Variance estimation is a fundamental problem in statistical modelling. In ultrahigh
dimensional linear regression where the dimensionality is much larger than the sample size …
dimensional linear regression where the dimensionality is much larger than the sample size …
Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models
J Fan, Y Ma, W Dai - Journal of the American Statistical Association, 2014 - Taylor & Francis
The varying coefficient model is an important class of nonparametric statistical model, which
allows us to examine how the effects of covariates vary with exposure variables. When the …
allows us to examine how the effects of covariates vary with exposure variables. When the …
Martingale difference correlation and its use in high-dimensional variable screening
In this article, we propose a new metric, the so-called martingale difference correlation, to
measure the departure of conditional mean independence between a scalar response …
measure the departure of conditional mean independence between a scalar response …