Assessment and Propagation of Model Uncertainty

By Denise Draper. 2011

1,689 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6673b0aefae40ce236bd8568e4e1a1483d27df46

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In Control and Systems Engineering

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Citations in the corpus

Prognostic Modeling with Logistic Regression Analysis (2001, methodology): “Also, Bayesian methods have been proposed,(17,45) which may especially be useful to express the uncertainty in predictions correctly, by averaging over alternative model specifications.(51) Also, shrinkage of regression coefficients results if a null effect is assumed as prior.”
The Variable Selection Problem (2000, background): “…means all, of the key developments of these Bayesian approaches to variable selection and model averaging have been discussed by Clyde (19991, Clyde, Parmigiani, and Vidakovic (1998), Draper (1995), George and McCulloch (1993,1997), Green (1995), and Hoeting, Madigan, Raftery, and Volinsky (1999).”
Parameterization and Bayesian Modeling (2004, methodology): “Data augmentation is designed to allow simulation-based computations to be performed more simply on the larger space of “complete data,” by analogy to the workings of the EM algorithm for maximum likelihood (Dempster, Laird, and Rubin 1977).”
On Inclusion-Driven Learning of Bayesian Networks (2003, methodology): “The need to account for the uncertainty of models (Draper, 1995) has led to the development of computational methods that implement the full Bayesian approach to modeling.”
Benchmark Priors for Bayesian Model Averaging (2001, methodology): “Firstly, we mention the fundamentally oriented statistics and econometrics literature on prior elicitation and model selection, such as exemplified in Box (1980), Zellner and Siow (1980), Draper (1995) and Phillips (1995) and the discussions of these papers.”
A Bayesian Formulation of Exploratory Data Analysis and Goodness‐of‐fit Testing * (2003, background): “Other places where vague statistical ideas have been formalized are in modeling missing data (Rubin, 1976) and model averaging (Draper, 1995; Raftery, 1995).”
Generalized additive models for location, scale and shape (2005, positive): “Conditioning on a single final model was criticized by Draper (1995) and Madigan and Raftery (1994) since it ignores model uncertainty and generally leads to an underestimation of the uncertainty about quantities of interest.”
Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach (2011, methodology): “…via decision theoretic approaches taking into consideration other costs (Fouskakis, Ntzoufras, and Draper 2009) or in Bayesian model averaging (BMA) using the full joint posterior distribution (see Draper 1995, Hoeting et al. 1999, or Clyde and George 2004 for reviews and additional references).”
Confidence sets for model selection by F -testing (2015, background): “In the literature of model selection, this issue is sometimes referred to as model selection uncertainty (Chat eld, 1995, Draper, 1995, Hoeting et al., 1999, Yuan and Yang, 2005).”
A Skew-Normal Mixture Regression Model (2014, background): “…& Enders, 2008), Bayesian information criterion (BIC; Schwarz, 1978), sample-adjusted BIC (SABIC; Sclove, 1987), and difference in BIC (DBIC; Draper, 1995); and two classificationbased criteria are entropy (Celeux & Soromenho, 1996) and integrated completed likelihood (ICL; Biernacki,…”
Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window (2008, methodology): “[9] Bayesian model selection [Kass and Raftery, 1995; Raftery, 1995; Gelman et al., 1996] and Bayesian model averaging (BMA) [Draper, 1995; Madigan and Raftery, 1994; Raftery, 1995; Hoeting et al., 1999; Brown et al., 2002] are naturally developed and linked through the posterior model probability…”
Displaying Uncertainty With Shading (2008, methodology): “As well as multiple regression and meta-analysis, potential applications of this technique include assessing the sensitivity of Bayesian posterior distributions to different prior assumptions, or the impact of uncertainties about model structure (Draper 1995).”
The Evolution of Forecast Density Combinations in Economics (2018, background): “Bayesian Model Averaging (BMA) offers a conceptually elegant means of dealing with model uncertainty and accordingly of producing forecast density combinations; e.g. see Roberts (1965) and Draper (1995).”
Modelling non-stationary natural mortality in catch-at-age models (2012, result): “When dealing with real observation data for which the state of nature is unknown, comparison between the commonly used individual models, the “best” model, and the differences between goodness-of-fit protects against overconfidence in the results (Draper, 1995).”