A split-and-conquer variable selection approach for high-dimensional general semiparametric models with massive data
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Publication:2111067
DOI10.1016/J.JMVA.2022.105128OpenAlexW4309351224MaRDI QIDQ2111067
Publication date: 23 December 2022
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2022.105128
Cites Work
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- A partially linear framework for massive heterogeneous data
- Aggregated estimating equation estimation
- Asymptotic properties for the semiparametric regression model with randomly censored data
- Additive partially linear models for massive heterogeneous data
- Nonconcave penalized likelihood with a diverging number of parameters.
- Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data
- Penalized empirical likelihood for semiparametric models with a diverging number of parameters
- Nonparametric Monte Carlo tests and their applications.
- Variable selection using MM algorithms
- Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis
- Penalized empirical likelihood and growing dimensional general estimating equations
- Penalized high-dimensional empirical likelihood
- Penalized Estimating Equations
- A split-and-conquer approach for analysis of
- Penalized Estimating Functions and Variable Selection in Semiparametric Regression Models
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Scalable Bootstrap for Massive Data
- A Split-and-Merge Bayesian Variable Selection Approach for Ultrahigh Dimensional Regression
- Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators
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