Marginal quantile regression for dependent data with a working odds-ratio matrix.
Sign Up to like & getrecommendations! Published in 2018 at "Biostatistics"
DOI: 10.1093/biostatistics/kxx052
Abstract: Dependent data arise frequently in applied research and several approaches to adjusting for the dependence among observations have been proposed in quantile regression. Cluster bootstrap is generally inefficient and computationally demanding, especially when the number… read more here.
Keywords: quantile regression; regression; dependent data; data working ... See more keywords
Cox regression with dependent error in covariates.
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DOI: 10.1111/biom.12741
Abstract: Many survival studies have error-contaminated covariates due to the lack of a gold standard of measurement. Furthermore, the error distribution can depend on the true covariates but the structure may be difficult to characterize; heteroscedasticity… read more here.
Keywords: regression; error; cox regression; dependent error ... See more keywords
High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting
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DOI: 10.5705/ss.202018.0044
Abstract: In recent years, extensive research has focused on the `1 penalized least squares (Lasso) estimators of high-dimensional linear regression when the number of covariates p is considerably larger than the sample size n. However, there… read more here.
Keywords: regression; errors covariates; high dimensional; regression dependent ... See more keywords