An elastic-net penalized expectile regression with applications

Xu, Qifa; Ding, Xiaoyi; Jiang, Cuixia; Yu, Keming; Shi, Lei

doi:10.1080/02664763.2020.1787355

Cited by 16 publications

(12 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2017), we shall use

{C}_n=\mathrm{loglog}(d)

and

{C}_n=\log (d)

in the simulation studies. Finally, one can also consider variable selection using penalization (Liao et al ., 2019; Xu et al ., 2021). However, Ma et al .…”

Section: Postscreening Model Uncertaintymentioning

confidence: 99%

“…For a comprehensive review on the related literature, see, for example Daouia, Girard, and Stupfler (2021), Xu et al . (2021) and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…(2019) and Xu et al . (2021). An algorithm to implement the EPCS is also presented, with the proper choice of the conditional set for each variable specified.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the above studies, the recent literature witnesses a surge of interest in both theoretical and empirical investigations based on expectile regressions. For example, Yang and Zou (2015) derive a regression tree-based gradient boosting estimator for non-parametric multiple expectile regressions; Zhao, Chen, and Yi (2018) and Liao, Park, and Choi (2019) consider the penalized linear expectile regression for fixed dimension, with applications to the Boston housing data; Gu and Zou (2016) systematically study the sparse asymmetric least squares regression under high dimensions; Xu et al (2021) propose an elastic-net penalized expectile regression, and provide analysis on clinical data; Tu and Wang (2020) study the jackknife model averaging for expectile regressions in increasing dimension. For a comprehensive review on the related literature, see, for example Daouia, Girard, and Stupfler (2021), Xu et al (2021) and references therein.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Variable Screening and Model Averaging for Expectile Regressions

Wang

2023

Oxf Bull Econ Stat

View full text Add to dashboard Cite

Expectile regression is a useful tool in modelling data with heterogeneous conditional distributions. This paper introduces two new concepts, i.e. the expectile correlation and expectile partial correlation, which can measure the contribution from each regressor to the response in expectile regression. In ultra-high dimensional setting, the expectile partial correlation, which provides an importance ranking of the predictors, is found useful for variable screening. Theoretical results indicate that the proposed screening procedure can achieve the sure screening set. Additionally, a model selection method via extended Bayesian information criterion (EBIC) and a jackknife model averaging (JMA) method are suggested after the screening step to address model uncertainty. The screening consistency of EBIC, the asymptotic optimality of JMA in the sense of minimizing out-of-sample expectile final prediction error, and the sparsity of JMA weight are then established. Finally, numerical results demonstrate the nice performance of our proposed methods.

show abstract

“…(2017), we shall use

{C}_n=\mathrm{loglog}(d)

and

{C}_n=\log (d)

in the simulation studies. Finally, one can also consider variable selection using penalization (Liao et al ., 2019; Xu et al ., 2021). However, Ma et al .…”

Section: Postscreening Model Uncertaintymentioning

confidence: 99%

“…For a comprehensive review on the related literature, see, for example Daouia, Girard, and Stupfler (2021), Xu et al . (2021) and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…(2019) and Xu et al . (2021). An algorithm to implement the EPCS is also presented, with the proper choice of the conditional set for each variable specified.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Variable Screening and Model Averaging for Expectile Regressions

Wang

2023

Oxf Bull Econ Stat

View full text Add to dashboard Cite

show abstract

“…The regression coefficients in 𝜔 can be estimated by optimizing the following elastic net penalty function as Equation (2). When using the elastic net penalty, we obtain the elastic net regression [44]:…”

Section: ) Elastic Net Regressionmentioning

confidence: 99%

An Improved-Bagging Model for Water Chemical Oxygen Demand Measurements Using UV-Vis Spectroscopy

Pan

Bian

et al. 2021

IEEE Access

View full text Add to dashboard Cite

The ultraviolet-visible (UV-Vis) spectroscopy measurement method of Chemical Oxygen Demand (COD) in water is a simple physical method that can measure water without secondary pollution from chemical reagents. To solve the problems of low accuracy and insufficient generalization capability of the COD prediction model, an improved Bagging algorithm is proposed and evaluated in this study. The Improved-Bagging algorithm can reduce model variance and bias concurrently, and improves the accuracy and stability of the traditional Bagging algorithm. Results show that the Improved-Bagging algorithm achieves a better prediction ability on different preprocessed data than the traditional Bagging algorithm. After ensemble empirical mode decomposition based (EEMD-Based) algorithm denoising and stability competitive adaptive reweighted sampling (SCARS) algorithm dimension reduction, Improved-Bagging model achieves the best prediction performance. Its coefficient of determination (R 2 ) on the prediction set reached 0.9317, its root mean square error of prediction (RMSEP) reached 5.39 mg/L, and its variance reached 5.53 mg 2 . Results also show that the Improved-Bagging algorithm can accurately measure the COD concentration in water, which lays the foundation for the wide application of spectroscopy to measure water quality parameters.

show abstract

M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality

Ranalli,

Salvati,

Petrella

et al. 2023

Biometrical J

View full text Add to dashboard Cite

In this work, we intersect data on size‐selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M‐quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M‐quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a B‐spline on the effect of the day of the year. Analytic and bootstrap‐based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand, model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution.

show abstract

An elastic-net penalized expectile regression with applications

Cited by 16 publications

References 90 publications

Variable Screening and Model Averaging for Expectile Regressions

Variable Screening and Model Averaging for Expectile Regressions

An Improved-Bagging Model for Water Chemical Oxygen Demand Measurements Using UV-Vis Spectroscopy

M‐quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality

Contact Info

Product

Resources

About