Jie Ding, Vahid Tarokh, Yuhong Yang
arXiv 22 Oct 2018 · Statistics — Machine Learning · publishedIEEE Signal Processing Magazine (2018) · 378 citations (OpenAlex)
arXiv:1810.09583 · PDF · DOI · OpenAlex · Extracted main text
In the era of big data, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this article is to bring a comprehensive overview of them, in terms of their motivation, large sample performance, and applicability. We provide integrated and practically relevant discussions on theoretical properties of state-of- the-art model selection approaches. We also share our thoughts on some controversial views on the practice of model selection.
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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 | J. Shao, “An asymptotic theory for linear model selection,” Statist.… (1997) An asymptotic theory for linear model selection | 1.000 | 10 | 4 | 100% |
| 2 | J. Ding, V. Tarokh, and Y. Yang, “Bridging AIC and BIC: a new criter… (2018) Bridging AIC and BIC: a new criterion for autoregression | 0.874 | 8 | 2 | 100% |
| 3 | R. Shibata, “Asymptotically efficient selection of the order of the… (1980) Asymptotically efficient selection of the order of the model for estimating parameters of a linear process | 0.843 | 3 | 3 | 100% |
| 4 | P. Stoica and Y. Selen, “Model-order selection: a review of informat… (2004) Model-order selection: a review of information criterion rules | 0.811 | 4 | 2 | 100% |
| 5 | J. Fan and R. Li, “Variable selection via nonconcave penalized likel… (2001) Variable selection via nonconcave penalized likelihood and its oracle properties | 0.737 | 3 | 2 | 100% |
| 6 | Y. Yang, “Can the strengths of AIC and BIC be shared? a conflict bet… (2005) Can the strengths of AIC and BIC be shared? a conflict between model indentification and regression estimation | 0.737 | 3 | 2 | 100% |
| 7 | Y. Yang, “Comparing learning methods for classification,” Stat. Sin.… (2006) Comparing learning methods for classification | 0.737 | 3 | 2 | 100% |
| 8 | J. Ding, V. Tarokh, and Y. Yang, “Bridging AIC and BIC: a new criter… (2016) Optimal variable selection in regression models | 0.644 | 2 | 2 | 100% |
| 9 | A. Barron, L. Birgé, and P. Massart, “Risk bounds for model selectio… (1999) Risk bounds for model selection via penalization | 0.644 | 2 | 2 | 100% |
| 10 | C.-K. Ing, “Accumulated prediction errors, information criteria and… (2007) Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 90 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2411.09258 | 0.644 | 2 | 2 |
| 2 | Regression Model Selection Under General Conditions | 0.644 | 2 | 2 |
| 3 | Tuning Parameter Selection in Econometrics | 0.405 | 1 | 1 |
| 4 | Model selection confidence sets for time series models with applications to electricity load data | 0.405 | 1 | 1 |