Bootstrapping Regression Models
By David Freedman. 1981
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- Fotios Petropoulos
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- Nikolaos Kourentzes
- Konstantinos Nikolopoulos
- Evangelos Spiliotis
- Vassilios Assimakopoulos
- Spyros Makridakis
- Christoph Bergmeir
Citations in the corpus
- Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, methodology): “This is a common approach in computer science, especially for machine learning (Bishop 2006: section 1.5; M. J. Kearns 1994:section 1.2), and also can be found in econometrics (White 1980) and statistics (Buja et al. 2018a; Freedman 1981).”
- Inference in Linear Regression Models with Many Covariates and Heteroscedasticity (2015, methodology): “An alternative, equally automatic approach is to employ the bootstrap and closely related resampling procedures (see, among others, Freedman (1981), Mammen 1993; Gonçalves and White 2005; Kline and Santos 2012).”
- Forecasting: theory and practice (2020, methodology): “Such models include, for example, the linear regression (Freedman, 1981) and autoregressive time series (Efron and Tibshirani, 1986).”
- Bootstrap Inference in Econometrics (2002, unknown): “The simplest way to deal with heteroskedasticity, which was originally proposed by Freedman (1981), is called bootstrapping pairs or the pairs bootstrap.”
- The Term Structure of Growth-at-Risk (2018, unknown): “These authors build on the so-called (y,x)-pairs bootstrap (Freedman, 1981) under which entire rows of data (containing the dependent and conditioning variables) are sampled with replacement, and demonstrate asymptotic feasibility under various assumptions for relative sizes N and T.”
- The dominance analysis approach for comparing predictors in multiple regression. (2003, methodology): “In the case of MR, the purpose of resampling is to capture the random component of the regression model, and a distinction is thus made between regression models involving fixed predictors and those involving random predictors (Efron, 1979; Freedman, 1981; Mooney & Duval, 1993; Neter et al., 1996; S”
- Model confidence bounds for variable selection (2016, methodology): “For example, residual bootstrap (Freedman, 1981) provides a valid bootstrap approximation to the sample distribution of the least square estimates.”