The Partial Linear Model in High Dimensions

DOI10.1111/sjos.12124zbMath1364.62196arXiv1307.1067OpenAlexW2963371463MaRDI QIDQ5251496

Sara van de Geer, Patric Müller

Publication date: 20 May 2015

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1307.1067

zbMATH Keywords

semiparametric modelvariable selectionLassonuisance functiondoubly penalized Lassohigh-dimensional partial linear model

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Generalized linear models (logistic models) (62J12)

Related Items

Nonlinear regression models with profile nonlinear least squares estimation, Partial index additive models with additive distortion measurement errors, Test for high dimensional partially linear models, A new test for high‐dimensional regression coefficients in partially linear models, Statistical inference on restricted partial linear regression models with partial distortion measurement errors, Learning rates for partially linear functional models with high dimensional scalar covariates, Estimation of semiparametric regression model with right-censored high-dimensional data, Generalized F-test for high dimensional regression coefficients of partially linear models, On the uniform convergence of empirical norms and inner products, with application to causal inference, Minimax optimal estimation in partially linear additive models under high dimension, Unnamed Item, Estimation and hypothesis test for partial linear multiplicative models, Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space, Doubly penalized estimation in additive regression with high-dimensional data, Learning rates for partially linear support vector machine in high dimensions, Penalized least squares estimation in the additive model with different smoothness for the components

Uses Software

• boost

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