Model Selection for Small Sample Regression

129 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Adaptive sparse polynomial chaos expansion based on least angle regression (2011, methodology): “In this section we focus on a correcting factor which has been derived in [46] for regression using a small experimental design.”
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017, methodology): “…scenarios, including but not limited to methods for general regression and time-series models (McQuarrie and Tsai 1998), for regression when the sample size is small (Chapelle et al. 2002), for Bayesian topical trend analysis (Masada et al. 2009) and for log-linear models (Foo et al. 2007).”