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Journal articles on the topic 'High-Dimensional Regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Zheng, Qi, Limin Peng, and Xuming He. "High dimensional censored quantile regression." Annals of Statistics 46, no. 1 (2018): 308–43. http://dx.doi.org/10.1214/17-aos1551.

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2

Izbicki, Rafael, and Ann B. Lee. "Converting high-dimensional regression to high-dimensional conditional density estimation." Electronic Journal of Statistics 11, no. 2 (2017): 2800–2831. http://dx.doi.org/10.1214/17-ejs1302.

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3

Lan, Wei, Hansheng Wang, and Chih-Ling Tsai. "Testing covariates in high-dimensional regression." Annals of the Institute of Statistical Mathematics 66, no. 2 (2013): 279–301. http://dx.doi.org/10.1007/s10463-013-0414-0.

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4

Meinshausen, Nicolai, Lukas Meier, and Peter Bühlmann. "p-Values for High-Dimensional Regression." Journal of the American Statistical Association 104, no. 488 (2009): 1671–81. http://dx.doi.org/10.1198/jasa.2009.tm08647.

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5

Li, Ker-Chau. "Nonlinear confounding in high-dimensional regression." Annals of Statistics 25, no. 2 (1997): 577–612. http://dx.doi.org/10.1214/aos/1031833665.

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6

Lin, Wei, and Jinchi Lv. "High-Dimensional Sparse Additive Hazards Regression." Journal of the American Statistical Association 108, no. 501 (2013): 247–64. http://dx.doi.org/10.1080/01621459.2012.746068.

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7

Giraud, Christophe, Sylvie Huet, and Nicolas Verzelen. "High-Dimensional Regression with Unknown Variance." Statistical Science 27, no. 4 (2012): 500–518. http://dx.doi.org/10.1214/12-sts398.

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8

Sun, Qiang, Hongtu Zhu, Yufeng Liu, and Joseph G. Ibrahim. "SPReM: Sparse Projection Regression Model For High-Dimensional Linear Regression." Journal of the American Statistical Association 110, no. 509 (2015): 289–302. http://dx.doi.org/10.1080/01621459.2014.892008.

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9

Wang, Siyang, and Hengjian Cui. "GeneralizedFtest for high dimensional linear regression coefficients." Journal of Multivariate Analysis 117 (May 2013): 134–49. http://dx.doi.org/10.1016/j.jmva.2013.02.010.

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10

Shen, Xiaotong, Wei Pan, Yunzhang Zhu, and Hui Zhou. "On constrained and regularized high-dimensional regression." Annals of the Institute of Statistical Mathematics 65, no. 5 (2013): 807–32. http://dx.doi.org/10.1007/s10463-012-0396-3.

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11

Zheng, Zemin, Yingying Fan, and Jinchi Lv. "High dimensional thresholded regression and shrinkage effect." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76, no. 3 (2013): 627–49. http://dx.doi.org/10.1111/rssb.12037.

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12

Maronna, Ricardo A. "Robust Ridge Regression for High-Dimensional Data." Technometrics 53, no. 1 (2011): 44–53. http://dx.doi.org/10.1198/tech.2010.09114.

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13

Cook, R. Dennis, Liliana Forzani, and Adam J. Rothman. "Prediction in abundant high-dimensional linear regression." Electronic Journal of Statistics 7 (2013): 3059–88. http://dx.doi.org/10.1214/13-ejs872.

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14

Azriel, D. "The conditionality principle in high-dimensional regression." Biometrika 106, no. 3 (2019): 702–7. http://dx.doi.org/10.1093/biomet/asz015.

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Summary Consider a high-dimensional linear regression problem, where the number of covariates is larger than the number of observations and the interest is in estimating the conditional variance of the response variable given the covariates. A conditional and an unconditional framework are considered, where conditioning is with respect to the covariates, which are ancillary to the parameter of interest. In recent papers, a consistent estimator was developed in the unconditional framework when the marginal distribution of the covariates is normal with known mean and variance. In the present wor

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15

Rajaratnam, Bala, Steven Roberts, Doug Sparks, and Honglin Yu. "Influence Diagnostics for High-Dimensional Lasso Regression." Journal of Computational and Graphical Statistics 28, no. 4 (2019): 877–90. http://dx.doi.org/10.1080/10618600.2019.1598869.

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16

Wager, Stefan, Wenfei Du, Jonathan Taylor, and Robert J. Tibshirani. "High-dimensional regression adjustments in randomized experiments." Proceedings of the National Academy of Sciences 113, no. 45 (2016): 12673–78. http://dx.doi.org/10.1073/pnas.1614732113.

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We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample–unbiased treatment effect estimates that leverages high-dimensional

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17

Sørensen, Øystein. "hdme: High-Dimensional Regression with Measurement Error." Journal of Open Source Software 4, no. 37 (2019): 1404. http://dx.doi.org/10.21105/joss.01404.

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18

Guo, Jianhua, Jianchang Hu, Bing-Yi Jing, and Zhen Zhang. "Spline-Lasso in High-Dimensional Linear Regression." Journal of the American Statistical Association 111, no. 513 (2016): 288–97. http://dx.doi.org/10.1080/01621459.2015.1005839.

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19

Cai, T. Tony, and Zijian Guo. "Accuracy assessment for high-dimensional linear regression." Annals of Statistics 46, no. 4 (2018): 1807–36. http://dx.doi.org/10.1214/17-aos1604.

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20

El Karoui, Noureddine, Derek Bean, Peter J. Bickel, Chinghway Lim, and Bin Yu. "On robust regression with high-dimensional predictors." Proceedings of the National Academy of Sciences 110, no. 36 (2013): 14557–62. http://dx.doi.org/10.1073/pnas.1307842110.

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21

Bean, Derek, Peter J. Bickel, Noureddine El Karoui, and Bin Yu. "Optimal M-estimation in high-dimensional regression." Proceedings of the National Academy of Sciences 110, no. 36 (2013): 14563–68. http://dx.doi.org/10.1073/pnas.1307845110.

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22

Aucott, Lorna S., Paul H. Garthwaite, and James Currall. "Regression methods for high dimensional multicollinear data." Communications in Statistics - Simulation and Computation 29, no. 4 (2000): 1021–37. http://dx.doi.org/10.1080/03610910008813652.

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23

Abramovich, Felix, and Vadim Grinshtein. "High-Dimensional Classification by Sparse Logistic Regression." IEEE Transactions on Information Theory 65, no. 5 (2019): 3068–79. http://dx.doi.org/10.1109/tit.2018.2884963.

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24

Wang, Tao, and Zhonghua Li. "Outlier detection in high-dimensional regression model." Communications in Statistics - Theory and Methods 46, no. 14 (2017): 6947–58. http://dx.doi.org/10.1080/03610926.2016.1140783.

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25

Lue, Heng-Hui, and Bing-Ran You. "High-dimensional regression analysis with treatment comparisons." Computational Statistics 28, no. 3 (2012): 1299–317. http://dx.doi.org/10.1007/s00180-012-0357-6.

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26

Fan, Zhaohu, and Matthew Reimherr. "High-dimensional adaptive function-on-scalar regression." Econometrics and Statistics 1 (January 2017): 167–83. http://dx.doi.org/10.1016/j.ecosta.2016.08.001.

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27

Gold, David, Johannes Lederer, and Jing Tao. "Inference for high-dimensional instrumental variables regression." Journal of Econometrics 217, no. 1 (2020): 79–111. http://dx.doi.org/10.1016/j.jeconom.2019.09.009.

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28

Spokoiny, Vladimir. "Variance Estimation for High-Dimensional Regression Models." Journal of Multivariate Analysis 82, no. 1 (2002): 111–33. http://dx.doi.org/10.1006/jmva.2001.2023.

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29

Datta, Abhirup, Hui Zou, and Sudipto Banerjee. "Bayesian high-dimensional regression for change point analysis." Statistics and Its Interface 12, no. 2 (2019): 253–64. http://dx.doi.org/10.4310/sii.2019.v12.n2.a6.

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30

Wei, Fengrong, and Jian Huang. "Consistent group selection in high-dimensional linear regression." Bernoulli 16, no. 4 (2010): 1369–84. http://dx.doi.org/10.3150/10-bej252.

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31

Shen, Weining, and Subhashis Ghosal. "Adaptive Bayesian density regression for high-dimensional data." Bernoulli 22, no. 1 (2016): 396–420. http://dx.doi.org/10.3150/14-bej663.

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32

Alquier, Pierre, and Mohamed Hebiri. "Generalization of constraints for high dimensional regression problems." Statistics & Probability Letters 81, no. 12 (2011): 1760–65. http://dx.doi.org/10.1016/j.spl.2011.07.011.

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33

Wang, Lie. "TheL1penalized LAD estimator for high dimensional linear regression." Journal of Multivariate Analysis 120 (September 2013): 135–51. http://dx.doi.org/10.1016/j.jmva.2013.04.001.

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34

Obozinski, Guillaume, Martin J. Wainwright, and Michael I. Jordan. "Support union recovery in high-dimensional multivariate regression." Annals of Statistics 39, no. 1 (2011): 1–47. http://dx.doi.org/10.1214/09-aos776.

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35

Gu, Yuwen, Jun Fan, Lingchen Kong, Shiqian Ma, and Hui Zou. "ADMM for High-Dimensional Sparse Penalized Quantile Regression." Technometrics 60, no. 3 (2018): 319–31. http://dx.doi.org/10.1080/00401706.2017.1345703.

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36

Zhu, Lixing, Baiqi Miao, and Heng Peng. "On Sliced Inverse Regression With High-Dimensional Covariates." Journal of the American Statistical Association 101, no. 474 (2006): 630–43. http://dx.doi.org/10.1198/016214505000001285.

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37

Wang, Hansheng. "Forward Regression for Ultra-High Dimensional Variable Screening." Journal of the American Statistical Association 104, no. 488 (2009): 1512–24. http://dx.doi.org/10.1198/jasa.2008.tm08516.

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38

Fang, Zhou, and Nicolai Meinshausen. "LASSO Isotone for High-Dimensional Additive Isotonic Regression." Journal of Computational and Graphical Statistics 21, no. 1 (2012): 72–91. http://dx.doi.org/10.1198/jcgs.2011.10095.

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39

Zheng, Qi, Colin Gallagher, and K. B. Kulasekera. "Adaptive penalized quantile regression for high dimensional data." Journal of Statistical Planning and Inference 143, no. 6 (2013): 1029–38. http://dx.doi.org/10.1016/j.jspi.2012.12.009.

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40

Strawn, N., A. Armagan, R. Saab, L. Carin, and D. Dunson. "Finite sample posterior concentration in high-dimensional regression." Information and Inference 3, no. 2 (2014): 103–33. http://dx.doi.org/10.1093/imaiai/iau003.

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41

Strawn, N., A. Armagan, R. Saab, L. Carin, and D. Dunson. "Finite sample posterior concentration in high-dimensional regression." Information and Inference 4, no. 1 (2014): 77. http://dx.doi.org/10.1093/imaiai/iau008.

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42

Jalali, Shirin, and Arian Maleki. "New approach to Bayesian high-dimensional linear regression." Information and Inference: A Journal of the IMA 7, no. 4 (2018): 605–55. http://dx.doi.org/10.1093/imaiai/iax016.

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Abstract Consider the problem of estimating parameters $X^n \in \mathbb{R}^n $, from $m$ response variables $Y^m = AX^n+Z^m$, under the assumption that the distribution of $X^n$ is known. Lack of computationally feasible algorithms that employ generic prior distributions and provide a good estimate of $X^n$ has limited the set of distributions researchers use to model the data. To address this challenge, in this article, a new estimation scheme named quantized maximum a posteriori (Q-MAP) is proposed. The new method has the following properties: (i) In the noiseless setting, it has similaritie

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43

Mukherjee, Rajarshi, Natesh S. Pillai, and Xihong Lin. "Hypothesis testing for high-dimensional sparse binary regression." Annals of Statistics 43, no. 1 (2015): 352–81. http://dx.doi.org/10.1214/14-aos1279.

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44

Xu, Min, Minhua Chen, and John Lafferty. "Faithful variable screening for high-dimensional convex regression." Annals of Statistics 44, no. 6 (2016): 2624–60. http://dx.doi.org/10.1214/15-aos1425.

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45

Nan, Ying, and Yuhong Yang. "Variable Selection Diagnostics Measures for High-Dimensional Regression." Journal of Computational and Graphical Statistics 23, no. 3 (2014): 636–56. http://dx.doi.org/10.1080/10618600.2013.829780.

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46

Ando, Tomohiro, and Ker-Chau Li. "A Model-Averaging Approach for High-Dimensional Regression." Journal of the American Statistical Association 109, no. 505 (2014): 254–65. http://dx.doi.org/10.1080/01621459.2013.838168.

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47

Datta, Abhirup, and Hui Zou. "CoCoLasso for high-dimensional error-in-variables regression." Annals of Statistics 45, no. 6 (2017): 2400–2426. http://dx.doi.org/10.1214/16-aos1527.

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48

Lozano, Aurélie C., Nicolai Meinshausen, and Eunho Yang. "Minimum Distance Lasso for robust high-dimensional regression." Electronic Journal of Statistics 10, no. 1 (2016): 1296–340. http://dx.doi.org/10.1214/16-ejs1136.

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49

Guha, Subharup, and Veerabhadran Baladandayuthapani. "A nonparametric Bayesian technique for high-dimensional regression." Electronic Journal of Statistics 10, no. 2 (2016): 3374–424. http://dx.doi.org/10.1214/16-ejs1184.

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50

Cook, R. Dennis, and Liliana Forzani. "Partial least squares prediction in high-dimensional regression." Annals of Statistics 47, no. 2 (2019): 884–908. http://dx.doi.org/10.1214/18-aos1681.

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