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Bayesian Model Selection in Social Research (with Discussion by Andrew Gelman & Donald B. Rubin, and Robert M. Hauser, and a Rejoinder)
 SOCIOLOGICAL METHODOLOGY 1995, EDITED BY PETER V. MARSDEN, CAMBRIDGE,; MASS.: BLACKWELLS.
, 1995
"... It is argued that Pvalues and the tests based upon them give unsatisfactory results, especially in large samples. It is shown that, in regression, when there are many candidate independent variables, standard variable selection procedures can give very misleading results. Also, by selecting a singl ..."
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Cited by 585 (21 self)
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single model, they ignore model uncertainty and so underestimate the uncertainty about quantities of interest. The Bayesian approach to hypothesis testing, model selection and accounting for model uncertainty is presented. Implementing this is straightforward using the simple and accurate BIC
A Simple Model of Capital Market Equilibrium with Incomplete Information
 JOURNAL OF FINANCE
, 1987
"... The sphere of modern financial economics encompases finance, micro investment theory and much of the economics of uncertainty. As is evident from its influence on other branches of economics including public finance, industrial organization and monetary theory, the boundaries of this sphere are both ..."
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Cited by 756 (2 self)
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The sphere of modern financial economics encompases finance, micro investment theory and much of the economics of uncertainty. As is evident from its influence on other branches of economics including public finance, industrial organization and monetary theory, the boundaries of this sphere
MODELING OF STRUCTURES WITH TAKING INTO ACCOUNT UNCERTAINTY OF THE PARAMETERS
, 2002
"... this paper a new method of calculation of safety of construction will be presented. Uncertainty of some parameters is modeled by using probabilistic methods, and some of the parameters are characterized by using of the safety factor ..."
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this paper a new method of calculation of safety of construction will be presented. Uncertainty of some parameters is modeled by using probabilistic methods, and some of the parameters are characterized by using of the safety factor
Model selection and accounting for model uncertainty in graphical models using Occam's window
, 1993
"... We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection o ..."
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Cited by 370 (47 self)
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We consider the problem of model selection and accounting for model uncertainty in highdimensional contingency tables, motivated by expert system applications. The approach most used currently is a stepwise strategy guided by tests based on approximate asymptotic Pvalues leading to the selection
Investing for the long run when returns are predictable
 Journal of Finance
, 2000
"... We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty, th ..."
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Cited by 444 (0 self)
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We examine how the evidence of predictability in asset returns affects optimal portfolio choice for investors with long horizons. Particular attention is paid to estimation risk, or uncertainty about the true values of model parameters. We find that even after incorporating parameter uncertainty
Accounting Uncertainties in the Optimal Design of MultiStage Hot Forging
"... Today, most of product designs employ sophisticated computer models and finite element analysis in their design. Most of these models are based on physical models without taking into account the uncertainties that occur during manufacturing. Forging is an industrial process extensively used in metal ..."
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Today, most of product designs employ sophisticated computer models and finite element analysis in their design. Most of these models are based on physical models without taking into account the uncertainties that occur during manufacturing. Forging is an industrial process extensively used
Taking into Account Uncertainties of Non Destructive Tests in the Estimation of Stochastic Degradation Processes
"... Abstract. Controlling the ageing of passive components and facilities implies a good knowledge of the various degradation phenomena and of their evolution. To achieve that goal, a mathematical model that describes degradation kinetics (such as a crack growth model) and enables the assessment of the ..."
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of the component’s remaining lifetime and its uncertainties is obviously useful. Such a tool is for instance needed to optimize the maintenance policy while preserving safety. Stochastic processes may be used to describe the random evolution of a degradation indicator around an average trend. Their construction
Anomalies in intertemporal choice: Evidence and an interpretation
 QUARTERLY JOURNAL OF ECONOMICS
, 1992
"... Research on decision making under uncertainty has been strongly influenced by the documentation of numerous expected utility (EU) anomaliesbehaviors that violate the expected utility axioms. The relative lack of progress on the closely related topic of intertemporal choice is partly due to the abse ..."
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Cited by 364 (2 self)
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Research on decision making under uncertainty has been strongly influenced by the documentation of numerous expected utility (EU) anomaliesbehaviors that violate the expected utility axioms. The relative lack of progress on the closely related topic of intertemporal choice is partly due
Evaluating Interval Forecasts
 International Economic Review
, 1997
"... This paper is intended to address the deficiency by clearly defining what is meant by a "good" interval forecast, and describing how to test if a given interval forecast deserves the label "good". One of the motivations of Engle's (1982) classic paper was to form dynamic int ..."
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Cited by 364 (11 self)
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fails to account for higherorder dynamics may be correct on average (have correct unconditional coverage), but in any given period it will have incorrect conditional coverage characterized by clustered outliers. These concepts will be defined precisely below, and tests for correct conditional coverage
Bayesian Model Averaging for Linear Regression Models
 Journal of the American Statistical Association
, 1997
"... We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem in ..."
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Cited by 325 (17 self)
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We consider the problem of accounting for model uncertainty in linear regression models. Conditioning on a single selected model ignores model uncertainty, and thus leads to the underestimation of uncertainty when making inferences about quantities of interest. A Bayesian solution to this problem
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