Matches in SemOpenAlex for { <https://semopenalex.org/work/W4292401934> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W4292401934 endingPage "25" @default.
- W4292401934 startingPage "1" @default.
- W4292401934 abstract "Varying coefficient model (VCM) is extensively used in various scientific fields due to its capability of capturing the changing structure of predictors. Classical mean regression analysis is often complicated in the existence of skewed, heterogeneous and heavy-tailed data. For this purpose, this work employs the idea of model averaging and introduces a novel comprehensive approach by incorporating quantile-adaptive weights across different quantile levels to further improve both least square (LS) and quantile regression (QR) methods. The proposed procedure that adaptively takes advantage of the heterogeneous and sparse nature of input data can gain more efficiency and be well adapted to extreme event case and high-dimensional setting. Motivated by its nice properties, we develop several robust methods to reveal the dynamic close-to-truth structure for VCM and consistently uncover the zero and nonzero patterns in high-dimensional scientific discoveries. We provide a new iterative algorithm that is proven to be asymptotic consistent and can attain the optimal nonparametric convergence rate given regular conditions. These introduced procedures are highlighted with extensive simulation examples and several real data analyses to further show their stronger predictive power compared with LS, composite quantile regression (CQR) and QR methods." @default.
- W4292401934 created "2022-08-20" @default.
- W4292401934 creator A5064798391 @default.
- W4292401934 creator A5069557245 @default.
- W4292401934 creator A5081970358 @default.
- W4292401934 creator A5089797194 @default.
- W4292401934 date "2022-08-09" @default.
- W4292401934 modified "2023-10-07" @default.
- W4292401934 title "Robust and sparse learning of varying coefficient models with high-dimensional features" @default.
- W4292401934 cites W1964894347 @default.
- W4292401934 cites W1965125844 @default.
- W4292401934 cites W1969752902 @default.
- W4292401934 cites W1996742192 @default.
- W4292401934 cites W2002414568 @default.
- W4292401934 cites W2020925091 @default.
- W4292401934 cites W2023500775 @default.
- W4292401934 cites W2027244596 @default.
- W4292401934 cites W2029469881 @default.
- W4292401934 cites W2029992122 @default.
- W4292401934 cites W2035416677 @default.
- W4292401934 cites W2038845890 @default.
- W4292401934 cites W2048924727 @default.
- W4292401934 cites W2060467039 @default.
- W4292401934 cites W2064850188 @default.
- W4292401934 cites W2069541127 @default.
- W4292401934 cites W2073767661 @default.
- W4292401934 cites W2074682976 @default.
- W4292401934 cites W2078269124 @default.
- W4292401934 cites W2086680598 @default.
- W4292401934 cites W2093049247 @default.
- W4292401934 cites W2112434835 @default.
- W4292401934 cites W2118711140 @default.
- W4292401934 cites W2119862467 @default.
- W4292401934 cites W2120875981 @default.
- W4292401934 cites W2124315825 @default.
- W4292401934 cites W2317235261 @default.
- W4292401934 cites W2472310269 @default.
- W4292401934 cites W2490054008 @default.
- W4292401934 cites W2562162676 @default.
- W4292401934 cites W3104903961 @default.
- W4292401934 cites W3124067556 @default.
- W4292401934 cites W3124920280 @default.
- W4292401934 doi "https://doi.org/10.1080/02664763.2022.2109129" @default.
- W4292401934 hasPublicationYear "2022" @default.
- W4292401934 type Work @default.
- W4292401934 citedByCount "0" @default.
- W4292401934 crossrefType "journal-article" @default.
- W4292401934 hasAuthorship W4292401934A5064798391 @default.
- W4292401934 hasAuthorship W4292401934A5069557245 @default.
- W4292401934 hasAuthorship W4292401934A5081970358 @default.
- W4292401934 hasAuthorship W4292401934A5089797194 @default.
- W4292401934 hasBestOaLocation W42924019342 @default.
- W4292401934 hasConcept C102366305 @default.
- W4292401934 hasConcept C105795698 @default.
- W4292401934 hasConcept C118671147 @default.
- W4292401934 hasConcept C126255220 @default.
- W4292401934 hasConcept C162324750 @default.
- W4292401934 hasConcept C2777303404 @default.
- W4292401934 hasConcept C33923547 @default.
- W4292401934 hasConcept C41008148 @default.
- W4292401934 hasConcept C50522688 @default.
- W4292401934 hasConcept C63817138 @default.
- W4292401934 hasConcept C83546350 @default.
- W4292401934 hasConceptScore W4292401934C102366305 @default.
- W4292401934 hasConceptScore W4292401934C105795698 @default.
- W4292401934 hasConceptScore W4292401934C118671147 @default.
- W4292401934 hasConceptScore W4292401934C126255220 @default.
- W4292401934 hasConceptScore W4292401934C162324750 @default.
- W4292401934 hasConceptScore W4292401934C2777303404 @default.
- W4292401934 hasConceptScore W4292401934C33923547 @default.
- W4292401934 hasConceptScore W4292401934C41008148 @default.
- W4292401934 hasConceptScore W4292401934C50522688 @default.
- W4292401934 hasConceptScore W4292401934C63817138 @default.
- W4292401934 hasConceptScore W4292401934C83546350 @default.
- W4292401934 hasFunder F4320321001 @default.
- W4292401934 hasLocation W42924019341 @default.
- W4292401934 hasLocation W42924019342 @default.
- W4292401934 hasOpenAccess W4292401934 @default.
- W4292401934 hasPrimaryLocation W42924019341 @default.
- W4292401934 hasRelatedWork W1502343398 @default.
- W4292401934 hasRelatedWork W1505751153 @default.
- W4292401934 hasRelatedWork W1977614578 @default.
- W4292401934 hasRelatedWork W2001322949 @default.
- W4292401934 hasRelatedWork W2056291818 @default.
- W4292401934 hasRelatedWork W2085554359 @default.
- W4292401934 hasRelatedWork W2922392237 @default.
- W4292401934 hasRelatedWork W3122882238 @default.
- W4292401934 hasRelatedWork W3124901906 @default.
- W4292401934 hasRelatedWork W35265094 @default.
- W4292401934 isParatext "false" @default.
- W4292401934 isRetracted "false" @default.
- W4292401934 workType "article" @default.