Subset Selection in Regression

By Anthony B. Atkinson. 1992

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Citations in the corpus

Best Subset Selection via a Modern Optimization Lens (2015, positive): “The Annals of Statistics 2016, Vol. 44, No. 2, 813–852 DOI: 10.1214/15-AOS1388 © Institute of Mathematical Statistics, 2016”
One-step Sparse Estimates in Nonconcave Penalized Likelihood Models (2008, unknown): “LIKELIHOOD MODELS1 BY HUI ZOU AND RUNZE LI”
A model selection approach for the identification of quantitative trait loci in experimental crosses (2002, methodology): “An and Gu (1985) showed that, when using BIC, and in the case of a fixed number of regressors, the backward elimination procedure is consistent, meaning that, as the sample size increases, the probability of choosing the correct model converges to 1.”
Gradient methods for minimizing composite functions (2012, methodology): “Recently, this technique became popular in signal processing and statistics [7,19].”
Robust stepwise regression (2002, methodology): “While it is easy to see that the Forward and Backward selection algorithms will stop in a ® nite number of steps, for the convergence of the Stepwise selection algorithm we follow Miller (1990).”
A working guide to boosted regression trees. (2008, methodology): “However, unimportant variables can be dropped using methods analogous to backward selection in regression (Miller 1990); these are sometimes referred to as recursive feature elimination.”
Adaptive Model Selection (2002, background): “In what follows, we discuss only the most relevant works (see Miller 1990 for a comprehensive summary of various selection procedures).”
An Introduction to Model Selection. (2000, methodology): “Apart from the final section on selection bias the material presented here is an outline of the first four chapters of Linhart and Zucchini (1986) with the technical details reduced to a minimum.”
Information-Theoretic Limits on Sparsity Recovery in the High-Dimensional and Noisy Setting (2007, background): “This sparsity recovery problem is of broad interest, arising in various areas, including subset selection in regression [24], structure estimation in graphical models [22], sparse approximation [10], [25], signal denoising [7], and compressive sensing [11], [5].”
Outlier Robust Model Selection in Linear Regression (2005, methodology): “A growing part of this literature is concerned with robust approaches to model selection (see, e.g., Hampel 1983; Ronchetti 1985, 1997; Ronchetti and Staudte 1994; Sommer and Staudte 1995; Sommer and Huggins 1996; Ronchetti, Field, and Blanchard 1997; Qian and Künsch 1998; Agostinelli 2002; Miller 2”
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches (2004, unknown): “…Dimensionality Reduction: Rough and Fuzzy-RoughBased Approaches Jensen, Richard; Shen, Qiang Published in: IEEE Transactions on Knowledge and Data Engineering DOI: 10.1109/TKDE.2004.96 Publication date: 2004 Citation for published version (APA): Jensen, R., & Shen, Q. (2004).”
On the LASSO and its Dual (2000, background): “Thus backward deletion is, practically, “built into” the LASSO and one could argue that it rather behaves like stepwise regression (Miller, 1990, Chapter 3.3).”
Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms (2009, methodology): “This inappropriate practice is widespread and may have arisen from a misunderstanding of the recommendation to split data sets in order to obtain unbiased estimates of parameter values after selecting models using the original subset (Miller 2002).”