Partition-based ultrahigh-dimensional variable screening
نویسندگان
چکیده
منابع مشابه
Feature Screening in Ultrahigh Dimensional Cox's Model.
Survival data with ultrahigh dimensional covariates such as genetic markers have been collected in medical studies and other fields. In this work, we propose a feature screening procedure for the Cox model with ultrahigh dimensional covariates. The proposed procedure is distinguished from the existing sure independence screening (SIS) procedures (Fan, Feng and Wu, 2010, Zhao and Li, 2012) in th...
متن کاملUltrahigh Dimensional Feature Screening via RKHS Embeddings
Feature screening is a key step in handling ultrahigh dimensional data sets that are ubiquitous in modern statistical problems. Over the last decade, convex relaxation based approaches (e.g., Lasso/sparse additive model) have been extensively developed and analyzed for feature selection in high dimensional regime. But in the ultrahigh dimensional regime, these approaches suffer from several pro...
متن کاملUltrahigh Dimensional Variable Selection: beyond the linear model
Variable selection in high-dimensional space characterizes many contemporary problems in scientific discovery and decision making. Many frequently-used techniques are based on independence screening; examples include correlation ranking or feature selection using a twosample t-test in high-dimensional classification. Within the context of the linear model, Fan and Lv (2008) showed that this sim...
متن کاملPenalized Composite Quasi-Likelihood for Ultrahigh-Dimensional Variable Selection.
In high-dimensional model selection problems, penalized least-square approaches have been extensively used. This paper addresses the question of both robustness and efficiency of penalized model selection methods, and proposes a data-driven weighted linear combination of convex loss functions, together with weighted L(1)-penalty. It is completely data-adaptive and does not require prior knowled...
متن کاملExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models
Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on correlation-based variable screening either impose strong statistical priors on...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrika
سال: 2017
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/asx052