FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2765449859> ?p ?o ?g. }

• W2765449859 abstract "Assessment and Propagation of Model Uncertainty By DAVID DRAPER* University of Bath, UK SUMMARY In most examples of inference and prediction, the expression of uncertainty about unknown quantities y on the basis of known quantities x is based on a model M that formalizes assumptions about how x and y are related. M will typically have two parts: structural assumptions S, such as the form of the link function and the choice of error distribution in a generalized linear model, and parameters 6 whose meaning is specific to a given choice of S. It is common in statistical theory and practice to acknowledge parametric uncertainty about 9 given a particular assumed structure S; it is less common to acknowledge structural uncertainty about S itself. A widely used approach, in fact, involves enlisting the aid of x to specify a plausible single best choice S* for S, and then proceeding as if S* were known to be correct. In general this approach fails to fully assess and propagate structural uncertainty, and may lead to miscali- brated uncertainty assessments about y given x. When miscalibration occurs it will often be in the direction of understatement of inferential or predictive uncertainty about y, leading to inaccurate scientific summaries and overconfi- dent decisions that do not incorporate sufficient hedging against uncertainty. In this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the U.S. Space Shuttle. Keywords: BAYES FACTORS; CALIBRATION; FORECASTING; HIERARCHICAL MODELS; INFERENCE; MODEL SPECIFICATION; OVER-FITTING; PREDICTION; ROBUSTNESS; SENSITIVITY ANALYSIS; UNCERTAINTY ASSESSMENT 1. INTRODUCTION The general framework of problems in inference and prediction involves two sets of ingredients: unknown (s) y—such as the causal effect of a treatment in inference, or the price of something next year in prediction—and known (s) x, which will typically include both data and context. The desire is usually to express uncertainty about y in light of x, for instance through a probability specification of the form p(yx). Specifications of this type that involve conditioning only on things that are known are rare, even in comparatively simple settings (e.g., Lindley, 1982); instead one typically appeals to a model M that formalizes judgments about how x and y are related. 1.1. Structural Uncertainty The model may be expressed (e.g., Draper et al., 1987; Hodges, 1987) in two parts as M = (S, 9), where S represents one or more sets of structural assumptions— such as a particular link function in a generalized linear model, or a particular form of heteroscedasticity or time dependence with non-IID data—and 9 represents parameters whose meaning is specific to the chosen structure(s). (It will often be possible to express a given model M in more than one way using this notation, but that does not affect the discussion that follows.) Once S is chosen, 9 typically follows ' Address for correspondence: Statistics Group, School of Mathematical Sciences, University of Bath, Claverton Down, Bath BA2 7AY, UK (d.draper@maths.bath.ac.uk)." @default.
• W2765449859 created "2017-11-10" @default.
• W2765449859 creator A5001965218 @default.
• W2765449859 date "2011-10-24" @default.
• W2765449859 modified "2023-09-23" @default.
• W2765449859 title "Assessment and Propagation of Model Uncertainty - eScholarship" @default.
• W2765449859 hasPublicationYear "2011" @default.
• W2765449859 type Work @default.
• W2765449859 sameAs 2765449859 @default.
• W2765449859 citedByCount "0" @default.
• W2765449859 crossrefType "journal-article" @default.
• W2765449859 hasAuthorship W2765449859A5001965218 @default.
• W2765449859 hasConcept C105795698 @default.
• W2765449859 hasConcept C107673813 @default.
• W2765449859 hasConcept C111472728 @default.
• W2765449859 hasConcept C11413529 @default.
• W2765449859 hasConcept C117251300 @default.
• W2765449859 hasConcept C119857082 @default.
• W2765449859 hasConcept C123614077 @default.
• W2765449859 hasConcept C12426560 @default.
• W2765449859 hasConcept C138885662 @default.
• W2765449859 hasConcept C14036430 @default.
• W2765449859 hasConcept C144237770 @default.
• W2765449859 hasConcept C149782125 @default.
• W2765449859 hasConcept C154945302 @default.
• W2765449859 hasConcept C176147448 @default.
• W2765449859 hasConcept C177803969 @default.
• W2765449859 hasConcept C2524010 @default.
• W2765449859 hasConcept C2776214188 @default.
• W2765449859 hasConcept C2780876879 @default.
• W2765449859 hasConcept C32230216 @default.
• W2765449859 hasConcept C33923547 @default.
• W2765449859 hasConcept C41008148 @default.
• W2765449859 hasConcept C78458016 @default.
• W2765449859 hasConcept C86803240 @default.
• W2765449859 hasConceptScore W2765449859C105795698 @default.
• W2765449859 hasConceptScore W2765449859C107673813 @default.
• W2765449859 hasConceptScore W2765449859C111472728 @default.
• W2765449859 hasConceptScore W2765449859C11413529 @default.
• W2765449859 hasConceptScore W2765449859C117251300 @default.
• W2765449859 hasConceptScore W2765449859C119857082 @default.
• W2765449859 hasConceptScore W2765449859C123614077 @default.
• W2765449859 hasConceptScore W2765449859C12426560 @default.
• W2765449859 hasConceptScore W2765449859C138885662 @default.
• W2765449859 hasConceptScore W2765449859C14036430 @default.
• W2765449859 hasConceptScore W2765449859C144237770 @default.
• W2765449859 hasConceptScore W2765449859C149782125 @default.
• W2765449859 hasConceptScore W2765449859C154945302 @default.
• W2765449859 hasConceptScore W2765449859C176147448 @default.
• W2765449859 hasConceptScore W2765449859C177803969 @default.
• W2765449859 hasConceptScore W2765449859C2524010 @default.
• W2765449859 hasConceptScore W2765449859C2776214188 @default.
• W2765449859 hasConceptScore W2765449859C2780876879 @default.
• W2765449859 hasConceptScore W2765449859C32230216 @default.
• W2765449859 hasConceptScore W2765449859C33923547 @default.
• W2765449859 hasConceptScore W2765449859C41008148 @default.
• W2765449859 hasConceptScore W2765449859C78458016 @default.
• W2765449859 hasConceptScore W2765449859C86803240 @default.
• W2765449859 hasLocation W27654498591 @default.
• W2765449859 hasOpenAccess W2765449859 @default.
• W2765449859 hasPrimaryLocation W27654498591 @default.
• W2765449859 hasRelatedWork W1540645508 @default.
• W2765449859 hasRelatedWork W1543872308 @default.
• W2765449859 hasRelatedWork W2008269009 @default.
• W2765449859 hasRelatedWork W2026542527 @default.
• W2765449859 hasRelatedWork W2070227331 @default.
• W2765449859 hasRelatedWork W2105299426 @default.
• W2765449859 hasRelatedWork W2173207635 @default.
• W2765449859 hasRelatedWork W2185864840 @default.
• W2765449859 hasRelatedWork W2194281066 @default.
• W2765449859 hasRelatedWork W2321648253 @default.
• W2765449859 hasRelatedWork W2491270614 @default.
• W2765449859 hasRelatedWork W2947640307 @default.
• W2765449859 hasRelatedWork W2991105449 @default.
• W2765449859 hasRelatedWork W3011100113 @default.
• W2765449859 hasRelatedWork W3190028650 @default.
• W2765449859 hasRelatedWork W320047127 @default.
• W2765449859 hasRelatedWork W3207652531 @default.
• W2765449859 hasRelatedWork W348377867 @default.
• W2765449859 hasRelatedWork W9559773 @default.
• W2765449859 hasRelatedWork W970611971 @default.
• W2765449859 isParatext "false" @default.
• W2765449859 isRetracted "false" @default.
• W2765449859 magId "2765449859" @default.
• W2765449859 workType "article" @default.