arXiv 16 May 2020 · Statistics — Methodology · publishedStat (2020) · 8 citations (OpenAlex)
arXiv:2005.08057 · PDF · DOI · OpenAlex · Extracted main text
We study the nested model averaging method on the solution path for a high-dimensional linear regression problem. In particular, we propose to combine model averaging with regularized estimators (e.g., lasso and SLOPE) on the solution path for high-dimensional linear regression. In simulation studies, we first conduct a systematic investigation on the impact of predictor ordering on the behavior of nested model averaging, then show that nested model averaging with lasso and SLOPE compares favorably with other competing methods, including the infeasible lasso and SLOPE with the tuning parameter optimally selected. A real data analysis on predicting the per capita violent crime in the United States shows an outstanding performance of the nested model averaging with lasso.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bruce E Hansen (2007) Least squares model averaging | 1.000 | 6 | 3 | 100% |
| 2 | Alan TK Wan, Xinyu Zhang, and Guohua Zou (2010) Least squares model averaging by Mallows criterion | 0.874 | 5 | 2 | 100% |
| 3 | Peng Zhao and Bin Yu (2006) On model selection consistency of Lasso | 0.843 | 3 | 3 | 100% |
| 4 | Magorzata Bogdan, Ewout Van Den Berg, Chiara Sabatti, Weijie Su, and… (2015) SLOPE - adaptive variable selection via convex optimization | 0.737 | 3 | 2 | 100% |
| 5 | Yang Feng, Qingfeng Liu, and Ryo Okui (2020) On the sparsity of Mallows model averaging estimator self | 0.644 | 2 | 2 | 100% |
| 6 | Robert Tibshirani (1996) Regression shrinkage and selection via the lasso | 0.644 | 2 | 2 | 100% |
| 7 | Martin J Wainwright (2009) Sharp thresholds for High-Dimensional and noisy sparsity recovery using $_1$-Constrained Quadratic Programming (Lasso) | 0.644 | 2 | 2 | 100% |
| 8 | Xinyu Zhang, Dalei Yu, Guohua Zou, and Hua Liang (2016) Optimal model averaging estimation for generalized linear models and generalized linear mixed-effects models | 0.644 | 2 | 2 | 100% |
| 9 | Alexandre Belloni and Victor Chernozhukov (2013) Least squares after model selection in high-dimensional sparse models | 0.511 | 2 | 1 | 100% |
| 10 | Weijie Su, Magorzata Bogdan, and Emmanuel Candes (2017) False discoveries occur early on the lasso path | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 32 scored citations.