Frequentist Model Average Estimators

By Nils Lid Hjort, Gerda Claeskens. 2003

736 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/395b5f21b34cdcc0811e7d7f17a310efb9255d7b

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In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

Model Averaging in Economics: An Overview (2015, neutral): “The FMA approach has started to receive attention over the last decade; see, for example, Hjort and Claeskens (2003) and Hansen (2007).”
Estimation and Accuracy after Model Selection (2013, methodology): “Hjort and Claeskens (2003) constructed an ambitious large-sample theory of frequentist model-selection estimation and model averaging, while making comparisons with Bayesian methods.”
Model averaging and weight choice in linear mixed-effects models (2014, methodology): “The corresponding results may be misleading (Buckland et al., 1997; Hjort & Claeskens, 2003; Claeskens & Hjort, 2008), and also may ignore useful information from the form of the relationship between response and covariates (Bates & Granger, 1969).”
Focused Information Criteria and Model Averaging for the Cox Hazard Regression Model (2006, result): “The theory and results for our FIC and model average estimators parallel the development and findings in our earlier articles (Claeskens and Hjort 2003; Hjort and Claeskens 2003a,b, hereafter referred to as CH and HC 2003).”
Model Averaging and Its Use in Economics (2017, methodology): “In the context of normal linear regression, Hoeting et al. (1996) propose a Bayesian method for simultaneous variable selection and outlier identification, using variable inflation to model outliers.”
Model averaging by jackknife criterion in models with dependent data (2013, methodology): “Liu (2011) recently derived some results on the asymptotic distribution of the JMA estimator assuming a linear model under local misspecification (Hjort and Claeskens, 2003).”
Generalized additive models for location, scale and shape (2005, background): “Averaging between selected models can reduce this underestimation; Hjort and Claeskens (2003).”
An asymptotic theory for model selection inference in general semiparametric problems (2007, positive): “Here we follow the approach leading to Theorem 4.1 of Hjort and Claeskens (2003), with the approach to inference following their Section 4.3.”
Optimal Model Assessment, Selection, and Combination (2006, methodology): “Hjort and Claeskens (2003) gave excellent reviews of various schemes for choosing weights, as well as their connection to Bayesian model averaging (BMA).”
Least Squares Model Averaging (2007, neutral): “For notational simplicity, all probability statements and expectations are conditional on the regressors X. From ( 19 ) we know that”
Combining Linear Regression Models (2005, methodology): “Other recent nonBayesian model averaging methods include FMA (Hjort and Claeskens 2003), in which limiting distributions and risk properties of the combined estimators are established under a local asymptotic framework, and a very interesting method based on unbiased risk estimation (Leung and Barro”
Identifying a Minimal Class of Models for High-dimensional Data (2015, methodology): “This approach was suggested both for the frequentist, Hjort & Claeskens (2003), and for the Bayesian, Hoeting et al. (1999), statistician.”
Bayesian inference in ecology (2004, background): “Frequentist model averaging is a nascent and promising area of statistical research (Claeskens & Hjort 2003; Hjort & Claeskens 2003 ), but it has not developed yet to the extent that it can be applied to even basic ecological problems.”