Results 31 to 40 of about 132 (45)
The shape of incomplete preferences
, 2006 Incomplete preferences provide the epistemic foundation for models of imprecise subjective probabilities and utilities that are used in robust Bayesian analysis and in theories of bounded rationality.
Nau, Robert
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Where do statistical models come from? Revisiting the problem of specification
, 2006 R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to be: specification, estimation and distribution. Since then the problem of statistical model specification has received scant attention in the statistics ...
Spanos, Aris
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Asymptotic inference for high-dimensional data
, 2010 In this paper, we study inference for high-dimensional data characterized by small sample sizes relative to the dimension of the data. In particular, we provide an infinite-dimensional framework to study statistical models that involve situations in ...
Kuelbs, Jim, Vidyashankar, Anand N.
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Extended statistical modeling under symmetry; the link toward quantum mechanics
, 2012 We derive essential elements of quantum mechanics from a parametric structure extending that of traditional mathematical statistics. The basic setting is a set $\mathcal{A}$ of incompatible experiments, and a transformation group $G$ on the cartesian ...
Helland, Inge S.
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Causal interpretation of stochastic differential equations
, 2014 We give a causal interpretation of stochastic differential equations (SDEs) by defining the postintervention SDE resulting from an intervention in an SDE.
Hansen, Niels Richard, Sokol, Alexander
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Multivariate log-concave distributions as a nearly parametric model [PDF]
, 2017 In this paper we show that the family Pd(lc) of probability distributions on ℝd with log-concave densities satisfies a strong continuity condition. In particular, it turns out that weak convergence within this family entails (i) convergence in total ...
Dümbgen, Lutz +2 more
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Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
, 2010 We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work
Dawid, A. Philip, Didelez, Vanessa
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Brittleness of Bayesian inference and new Selberg formulas [PDF]
, 2014 The incorporation of priors in the Optimal Uncertainty Quantification (OUQ) framework \cite{OSSMO:2011} reveals brittleness in Bayesian inference; a model may share an arbitrarily large number of finite-dimensional marginals with, or be arbitrarily close
Owhadi, Houman, Scovel, Clint
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On the frequentist and Bayesian approaches to hypothesis testing [PDF]
, 2006 Hypothesis testing is a model selection problem for which the solution proposed by the two main statistical streams of thought, frequentists and Bayesians, substantially differ.
Girón, F. Javier, Moreno, Elías
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Comment on: “Decision-theoretic foundations for statistical causality”
Journal of Causal Inference, 2022 Shpitser Ilya
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