Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
نویسندگان
چکیده
منابع مشابه
Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension.
Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the ...
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The tables of the appendix provide additional numerical results. Table 1 summarizes simulation results for Q-SCAD, LS-SCAD, Q-MCP, LS-MCP with sample sizes 50, 100 and 200 for modeling the 0.7 conditional quantile for the heteroscedastic error setting described in Section 4 of the main paper. The MCP approaches, Q-MCP and LS-MCP, are the equivalent of Q-SCAD and LS-SCAD with the SCAD penalty fu...
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Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2012
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2012.656014