High-dimensional partially linear model
WebPartially linear models attract much attention to investigate the association between predictors and the response variable when the dependency on some predictors may be … Web25 de mar. de 2024 · @article{osti_1969272, title = {Bi-Fidelity Modeling of Uncertain and Partially Unknown Systems Using DeepONets}, author = {De, Subhayan and Reynolds, Matthew and Hassanaly, Malik and King, Ryan N. and Doostan, Alireza}, abstractNote = {Recent advances in modeling large-scale, complex physical systems have shifted …
High-dimensional partially linear model
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Web20 de jun. de 2024 · Single-index models are potentially important tools for multivariate nonparametric regression analysis. They generalize linear regression models by replacing the linear combination \(\alpha^T_0\) with a nonparametric component \(\eta_0({\alpha^T_0})X\), where \(\eta_0(\cdot)\) is an unknown univariate link function. … WebWe consider a flexible semiparametric approach, namely, partially linear single-index models, for ultra high-dimensional longitudinal data. Most importantly, we allow not only the partially linear covariates but also the single-index covariates within the unknown flexible function estimated nonparametrically to be ultra high dimensional.
Web10 de set. de 2024 · Distributed Partially Linear Additive Models With a High Dimensional Linear Part Abstract: We study how the divide and conquer principle works in high-dimensional partially linear additive models when the dimension of the linear part is … Web18 de ago. de 2024 · To address these issues, the current paper proposes a new communication-efficient distributed learning algorithm for partially sparse linear models with an increasing number of features. The proposed method is based on the classical divide and conquer strategy for handing big data and each sub-method defined on each …
Web1 de out. de 2024 · Guo et al. (2024) study a statistical inference procedure in the high-dimensional linear mediation models. They propose a new F-type test for the direct and indirect effects and also develop its theoretical properties. Luo et al. (2024) extend the methodologies to a survival outcome for settings with high-dimensional mediators. WebWe consider a flexible semiparametric approach, namely, partially linear single-index models, for ultra high-dimensional longitudinal data. Most importantly, we allow not …
Web29 de mar. de 2024 · We consider a semiparametric additive partially linear regression model (APLM) for analysing ultra-high-dimensional data where both the number of …
Webtion in partially linear models with a divergent number of covariates in the linear part, under the assumption that the vector of regression coefficients is sparse. We apply the … durham county nc child supportWeb3 de jul. de 2013 · It is shown that a high‐dimensional linear part can be estimated with oracle rates, using the least absolute shrinkage and selection operator penalty for the linear part and a smoothness Penalty for the nonparametric part. Partial linear models have been widely used as flexible method for modelling linear components in conjunction with … cryptocomesWebsult empirically on several high-dimensional multiple regression and classification problems. 1 Introduction Hierarchical modeling is a mainstay of Bayesian inference. For instance, in (generalized) linear models, the unknown parameters are effects, each of which describes the association of a particular covariate with a response of interest. durham county nc clerk of courtWebvariable selection in high-dimensional partially faithful linear models under assumptions on the design matrix that are very different from coherence assumptions for penalty-based methods. The pc-simple algorithm can also be viewed as a generalization of correlation screening or sure independence screening (Fan & Lv, 2008). crypto.com earn minimumWebKeywords: High dimension; minimax optimal; partial linear additive model; semiparametric. 1. Introduction In this paper, we consider high dimensional partially linear additive models: Y = X T 0 + XJ j =1 fj (Z j)+ "; (1.1) where the Euclidean vector 0 2 R p is sparse with p > n and fj: R 7! R are nonparametric functions with possibly di erent ... crypto.com earningWebHigh Dimensional Inference in Partially Linear Models zero. Instead, we propose two modi ed versions of the debiased Lasso estimators for 0. Both versions are shown to be … durham county nc gis tax bill searchWebIn this paper, we study ultra-high-dimensional partially linear models when the dimension of the linear predictors grows exponentially with the sample size. For the variable screening, we propose a sequential profile Lasso method (SPLasso) and show that it possesses the screening property. durham county nc district attorney