Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model

By Nils Lid Hjort, Gerda Claeskens. 2006

133 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0424045c4aa02427f586c62da6687ccd666fd577

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In Statistics and Probability

Citations in the corpus

Focused information criterion and model averaging for generalized additive partial linear models (2011, methodology): “As argued in Hjort and Claeskens (2006), a direct construction of a confidence interval based on Theorem 3 may not be easy.”
Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window (2008, methodology): “[40] The scaling factor in equation (20) is similar to the smoothing parameter in smoothed FIC and AIC [Hjort and Claeskens, 2003, 2006], which dictates the amount of smoothing between candidate models.”
Model averaging by jackknife criterion in models with dependent data (2013, methodology): “Contributions to model averaging from a fully-fledged 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…”
Focused Information Criteria, Model Selection, and Model Averaging in a Tobit Model With a Nonzero Threshold (2012, background): “There is a large collection of Bayesian literature on model averaging (see, e.g., Hoeting et al. 1999 for a review), but relatively few studies from a frequentist perspective; however, some significant progress has been made in recent years.”
Weighted‐Average Least Squares (WALS): A Survey (2016, methodology): “…regression setups cover a variety of models such as logistic regression models (Claeskens et al., 2006); models for survival analysis (Hjort and Claeskens, 2006); generalized additive partial linear models (Zhang and Liang, 2011); Tobit models (Zhang et al., 2012); multinomial and ordered…”
Model averaging based on leave-subject-out cross-validation (2016, methodology): “…development of frequentist model averaging methods such as weighting strategy based on the scores of information 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),…”
A Mallows-Type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model (2018, background): “There is also room for an extension of the present approach to the generalized varyingcoefficient partially linear model (Li and Liang 2008; Lam and Fan 2008) that permits a discrete response variable, and more versatile link functions for error distributions.”