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Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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2
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Su M, Wang Q. A convex programming solution based debiased estimator for quantile with missing response and high-dimensional covariables. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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3
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Cao Z, Kang X, Wang M. Doubly robust weighted composite quantile regression based on SCAD‐
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2. CAN J STAT 2021. [DOI: 10.1002/cjs.11681] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Zhimiao Cao
- Department of Statistics Yunnan University Kunming, Yunnan 650504 P.R. China
- School of Statistics Qufu Normal University Qufu, Shandong 273165 P.R. China
| | - Xiaoning Kang
- International Business College and Institute of Supply Chain Analytics Dongbei University of Finance and Economics Dalian, Liaoning 116025 P.R. China
| | - Mingqiu Wang
- School of Statistics Qufu Normal University Qufu, Shandong 273165 P.R. China
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4
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Liu J, Xu W, Lian H. Convergence rate for nonparametric quantile regression with a total variation penalty. Stat (Int Stat Inst) 2021. [DOI: 10.1002/sta4.361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Jiamin Liu
- School of Statistics Renmin University of China Beijing China
- Department of Mathematics City University of Hong Kong Hong Kong China
| | - Wangli Xu
- School of Statistics Renmin University of China Beijing China
| | - Heng Lian
- Department of Mathematics City University of Hong Kong Hong Kong China
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5
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Pietrosanu M, Shu H, Jiang B, Kong L, Heo G, He Q, Gilmore J, Zhu H. Estimation for the bivariate quantile varying coefficient model with application to diffusion tensor imaging data analysis. Biostatistics 2021; 24:465-480. [PMID: 34418057 PMCID: PMC10102902 DOI: 10.1093/biostatistics/kxab031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 11/13/2022] Open
Abstract
Despite interest in the joint modeling of multiple functional responses such as diffusion properties in neuroimaging, robust statistical methods appropriate for this task are lacking. To address this need, we propose a varying coefficient quantile regression model able to handle bivariate functional responses. Our work supports innovative insights into biomedical data by modeling the joint distribution of functional variables over their domains and across clinical covariates. We propose an estimation procedure based on the alternating direction method of multipliers and propagation separation algorithms to estimate varying coefficients using a B-spline basis and an $L_2$ smoothness penalty that encourages interpretability. A simulation study and an application to a real-world neurodevelopmental data set demonstrates the performance of our model and the insights provided by modeling functional fractional anisotropy and mean diffusivity jointly and their association with gestational age and sex.
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Affiliation(s)
- Matthew Pietrosanu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
| | - Haoxu Shu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
| | - Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
| | - Giseon Heo
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle,WA 98109, USA
| | - John Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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6
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Alharthi M, Dogan E, Taskin D. Analysis of CO 2 emissions and energy consumption by sources in MENA countries: evidence from quantile regressions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:38901-38908. [PMID: 33745049 PMCID: PMC7980797 DOI: 10.1007/s11356-021-13356-0] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 03/04/2021] [Indexed: 05/05/2023]
Abstract
The development of economies and energy usage can significantly impact the carbon dioxide (CO2) emissions in the Middle East and North Africa (MENA) countries. Therefore, this study aims to analyze the factors that determine CO2 emissions in MENA under the environmental Kuznets curve (EKC) framework by applying novel quantile techniques on data for CO2 emissions, real income, renewable and non-renewable energy consumption, and urbanization over the period from 1990 to 2015. The results from the estimations suggest that renewable energy consumption significantly reduces the level of emissions; furthermore, its impact increases with higher quantiles. In addition, non-renewable energy consumption increases CO2 emissions, while its magnitude decreases with higher quantiles. The empirical results also confirm the validity of EKC hypothesis for the panel of MENA economies. Policymakers in the region should implement policies and regulations to promote the adoption and use of renewable energy to mitigate carbon emissions.
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Affiliation(s)
- Majed Alharthi
- Finance Department, College of Business, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia
| | - Eyup Dogan
- Department of Finance and Economics, University of Sharjah, Sharjah, UAE.
- Department of Economics, Abdullah Gul University, Kayseri, Turkey.
| | - Dilvin Taskin
- Faculty of Business, Yasar University, İzmir, Turkey
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7
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Pietrosanu M, Zhang L, Seres P, Elkady A, Wilman AH, Kong L, Cobzas D. Stable Anatomy Detection in Multimodal Imaging Through Sparse Group Regularization: A Comparative Study of Iron Accumulation in the Aging Brain. Front Hum Neurosci 2021; 15:641616. [PMID: 33708081 PMCID: PMC7940836 DOI: 10.3389/fnhum.2021.641616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
Multimodal neuroimaging provides a rich source of data for identifying brain regions associated with disease progression and aging. However, present studies still typically analyze modalities separately or aggregate voxel-wise measurements and analyses to the structural level, thus reducing statistical power. As a central example, previous works have used two quantitative MRI parameters-R2* and quantitative susceptibility (QS)-to study changes in iron associated with aging in healthy and multiple sclerosis subjects, but failed to simultaneously account for both. In this article, we propose a unified framework that combines information from multiple imaging modalities and regularizes estimates for increased interpretability, generalizability, and stability. Our work focuses on joint region detection problems where overlap between effect supports across modalities is encouraged but not strictly enforced. To achieve this, we combine L 1 (lasso), total variation (TV), and L 2 group lasso penalties. While the TV penalty encourages geometric regularization by controlling estimate variability and support boundary geometry, the group lasso penalty accounts for similarities in the support between imaging modalities. We address the computational difficulty in this regularization scheme with an alternating direction method of multipliers (ADMM) optimizer. In a neuroimaging application, we compare our method against independent sparse and joint sparse models using a dataset of R2* and QS maps derived from MRI scans of 113 healthy controls: our method produces clinically-interpretable regions where specific iron changes are associated with healthy aging. Together with results across multiple simulation studies, we conclude that our approach identifies regions that are more strongly associated with the variable of interest (e.g., age), more accurate, and more stable with respect to training data variability. This work makes progress toward a stable and interpretable multimodal imaging analysis framework for studying disease-related changes in brain structure and can be extended for classification and disease prediction tasks.
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Affiliation(s)
- Matthew Pietrosanu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Li Zhang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Peter Seres
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Ahmed Elkady
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Dana Cobzas
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.,Department of Computer Science, MacEwan University, Edmonton, AB, Canada
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8
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Su T, Wang Y, Liu Y, Branton WG, Asahchop E, Power C, Jiang B, Kong L, Tang N. Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1257. [PMID: 33287025 PMCID: PMC7712546 DOI: 10.3390/e22111257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/26/2020] [Accepted: 11/02/2020] [Indexed: 11/21/2022]
Abstract
Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.
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Affiliation(s)
- Tong Su
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming 650091, China;
| | - Yafei Wang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (Y.W.); (Y.L.); (B.J.)
| | - Yi Liu
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (Y.W.); (Y.L.); (B.J.)
| | - William G. Branton
- Department of Medicine (Neurology), University of Alberta, Edmonton, AB T6G 2G1, Canada; (W.G.B.); (E.A.); (C.P.)
| | - Eugene Asahchop
- Department of Medicine (Neurology), University of Alberta, Edmonton, AB T6G 2G1, Canada; (W.G.B.); (E.A.); (C.P.)
| | - Christopher Power
- Department of Medicine (Neurology), University of Alberta, Edmonton, AB T6G 2G1, Canada; (W.G.B.); (E.A.); (C.P.)
| | - Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (Y.W.); (Y.L.); (B.J.)
| | - Linglong Kong
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada; (Y.W.); (Y.L.); (B.J.)
| | - Niansheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming 650091, China;
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10
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Ciuperca G, Maciak M, Wahl F. Detection of similar successive groups in a model with diverging number of variable groups. Seq Anal 2020. [DOI: 10.1080/07474946.2020.1726687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Gabriela Ciuperca
- Institut Camille Jordan, UMR 5208, Université Claude Bernard Lyon 1, France
| | - Matúš Maciak
- Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic
| | - François Wahl
- Institut Camille Jordan, UMR 5208, Université Claude Bernard Lyon 1, France
- IFPEN, BP3, Solaize, France
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11
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Time-varying quantile single-index model for multivariate responses. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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12
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Song A, Ma T, Lv S, Lin C. A model-free variable selection method for reducing the number of redundant variables. STATISTICS-ABINGDON 2018. [DOI: 10.1080/02331888.2018.1515949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Anchao Song
- School of Statistics, Southwestern University of Finance and Economics, ChengDu, China
| | - Tiefeng Ma
- School of Statistics, Southwestern University of Finance and Economics, ChengDu, China
| | - Shaogao Lv
- School of Statistics and Mathematics, Nanjing Audit University, Nanjing, China
| | - Changsheng Lin
- School of Mathematics and Statistics, Yangtze Normal University, Chongqing, China
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