Optimal Weight Choice for Frequentist Model Average Estimators

By Hua Liang, Guohua Zou, Alan T. K. Wan, Xinyu Zhang. 2011

220 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/088adb7c7a12bdd91315406416d023a4c935c4e1

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In Statistics and Probability

Citations in the corpus

Model averaging and weight choice in linear mixed-effects models (2014, methodology): “More recently, under linear regression models, Liang et al. (2011) introduced a general random weight form that includes smoothed AIC, smoothed BIC and many other weights as special cases.”
Generalized Least Squares Model Averaging (2016, unknown): “Frequentist model averaging has also become important recently; see, e.g., Hjort and Claeskens (2003) and Liang et al. (2011).”
The Forecast Combination Puzzle: A Simple Theoretical Explanation (2016, methodology): “This is the case in Bates and Granger (1969) and it remains the case in later contributions, important and insightful as they may be, such as Hansen (2008), Elliott (2011), Liang et al. (2011), and Hsiao and Wan (2014).”
A Mallows-Type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model (2018, background): “See, for example, Wan, Zhang, and Zou (2010) and Liang et al. (2011).”
A flexible semiparametric forecasting model for time series (2015, methodology): “A popular method is to use model averaging whereby we fit a number of candidate models and then weight them according to some criterion (see, for example, Hansen 2007, Liang et al 2011).”
Model averaging by jackknife criterion in models with dependent data (2013, background): “…frequentist standpoint were made by Buckland et al. (1997), Yang (2001), Hjort and Claeskens (2003, 2006), Yuan and Yang (2005), Hansen (2007, 2008), Goldenshluger (2009), Schomaker et al. (2010), Wan et al. (2010), Liang et al. (2011), Zhang and Liang (2011), Zhang et al. (2012), among others.”
Model averaging based on leave-subject-out cross-validation (2016, methodology): “…criteria (Buckland et al., 1997; Hjort and Claeskens, 2003, 2006; Zhang and Liang, 2011; Zhang et al., 2012; Xu et al., 2014), the adaptive regression by mixing by Yang (2001), Mallows model averaging (MMA) by Hansen (2007), and optimal mean squared error averaging by Liang et al. (2011).”
Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models (2016, methodology): “See, for example, Mallows model averaging (Hansen 2007), optimal mean squared error averaging (Liang et al. 2011), jackknife model averaging (Hansen and Racine 2012), heteroskedasticityrobust Cp (Liu and Okui 2013), and optimal model averaging for linear mixed-effects models (Zhang, Zou, and Liang…”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, unknown): “The model (1) is widely used in the model averaging literature, for example, Magnus, Powell, and Prüfer (2010), Liang et al. (2011), and Liu (2015).”
Complete subset regressions (2013, methodology): “Liang et al. (2011) consider linear models with serially independent homoskedastic normal errors and estimate combination weights through a procedure designed to minimize the trace of the MSE of the parameter vector estimates.”