Model averaging marginal regression for high dimensional conditional quantile prediction

Tu, Jingwen; Yang, Hu; Guo, Chaohui; Lv, Jing

doi:10.1007/s00362-020-01212-1

Cited by 1 publication

(1 citation statement)

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“…Lan et al (2018) developed a sequential model averaging approach to make stable predictions for UHD linear regression models. Tu et al (2021) considered a high-dimensional semiparametric model averaging approach. To overcome the drawbacks of the two-step model averaging procedure, extending UHD forecasting to the context of conditional quantile estimation may help provide a more complete picture of the conditional distribution of the response given all candidate covariates, which makes it more flexible to accommodate heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

High‐dimensional model averaging for quantile regression

et al. 2023

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This article considers robust prediction issues in ultrahigh‐dimensional (UHD) datasets and proposes combining quantile regression with sequential model averaging to arrive at a quantile sequential model averaging (QSMA) procedure. The QSMA method is made computationally feasible by employing a sequential screening process and a Bayesian information criterion (BIC) model averaging method for UHD quantile regression and provides a more accurate and stable prediction of the conditional quantile of a response variable. Meanwhile, the proposed method shows effective behaviour in dealing with prediction in UHD datasets and saves a great deal of computational cost with the help of the sequential technique. Under some suitable conditions, we show that the proposed QSMA method can mitigate overfitting and yields reliable predictions. Numerical studies, including extensive simulations and a real data example, are presented to confirm that the proposed method performs well.

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Section: Introductionmentioning

confidence: 99%