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NONPARAMETRIC PRIORS ON COMPLETE SEPARABLE METRIC SPACES
"... A Bayesian model is nonparametric if its parameter space has infinite dimension; typical choices are spaces of discrete measures and Hilbert spaces. We consider the construction of nonparametric priors when the parameter takes values in a more general functional space. We (i) give a Prokhorovtype r ..."
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Cited by 2 (0 self)
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A Bayesian model is nonparametric if its parameter space has infinite dimension; typical choices are spaces of discrete measures and Hilbert spaces. We consider the construction of nonparametric priors when the parameter takes values in a more general functional space. We (i) give a Prokhorov
A Nonparametric Prior for Simultaneous Covariance Estimation
"... In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. In many cases, one assumes that the covariance (or correlation) structure is the same for all groups. However, this assumption, if it fails to hold, can have an advers ..."
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of possibilities (e.g., structural zeros and/or commonality of individual parameters across groups). In this paper we develop a family of nonparametric priors using the matrix stickbreaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms
Nonparametric Prior Elicitation with Imprecisely assessed Probabilities
, 2006
"... A crucial question that might be arisen in the elicitation of the expert’s probability is that is the expert able to specify the probability with absolute precision? Unfortunately, by reviewing the statistical and psychological literature, we learned that the most individuals, in particular the expe ..."
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A crucial question that might be arisen in the elicitation of the expert’s probability is that is the expert able to specify the probability with absolute precision? Unfortunately, by reviewing the statistical and psychological literature, we learned that the most individuals, in particular the experts, cannot elicit probabilities with absolute precision.
On a class of Bayesian nonparametric priors derived by subordination of Stable processes
"... We investigate a new class of Bayesian nonparametric priors derived by convolution mixture (composition) of the (positive) Stable r.v. by an independent ID r.v. belonging to the family of Generalized Gamma convolutions (Bondesson, 1992). We rely on the study proposed in James (2006) and on recent r ..."
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We investigate a new class of Bayesian nonparametric priors derived by convolution mixture (composition) of the (positive) Stable r.v. by an independent ID r.v. belonging to the family of Generalized Gamma convolutions (Bondesson, 1992). We rely on the study proposed in James (2006) and on recent
NONPARAMETRIC PRIORS FOR ORDINAL BAYESIAN SOCIAL SCIENCE MODELS: SPECIFICATION AND ESTIMATION
"... A generalized linear mixed model, and ordered probit, is used to estimate levels of stress in presidential political appointees as a means of understanding their surprising short tenures. A Bayesian approach is used, where the random effects are modeled with a Dirichlet process mixture prior, allowi ..."
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Cited by 7 (2 self)
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approach may be a step in the direction of satisfying both camps. We give a detailed description of the data, show how to implement the model, and describe some interesting conclusions. The model utilizing a nonparametric prior fits better and reveals more information in the data.
Nonparametric estimation of average treatment effects under exogeneity: a review
 REVIEW OF ECONOMICS AND STATISTICS
, 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
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Cited by 597 (26 self)
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Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functionalform assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this
Statistical classification of buried unexploded ordnance using nonparametric prior models
 IEEE Transactions on Geoscience and Remote Sensing
, 2007
"... Abstract—We used kernel density estimation (KDE) methods to build a priori probability density functions (pdfs) for the vector of features that are used to classify unexploded ordnance items given electromagneticinduction sensor data. This a priori information is then used to develop a new suite of ..."
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Cited by 3 (0 self)
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of estimation and classification algorithms. As opposed to the commonly used maximumlikelihood parameter estimation methods, here we employ a maximum a posteriori (MAP) estimation algorithm that makes use of KDEgenerated pdfs. Similarly, we use KDE priors to develop a suite of classification schemes
Results 1  10
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117,901