scholarly journals Posterior Propriety for Hierarchical Models with Log-Likelihoods That Have Norm Bounds

2016 ◽  
Vol 11 (2) ◽  
pp. 545-571 ◽  
Author(s):  
Sarah E. Michalak ◽  
Carl N. Morris
Keyword(s):  
Hierarchical Models ◽  
Posterior Propriety

scholarly journals Posterior propriety and admissibility of hyperpriors in normal hierarchical models

2005 ◽  
Vol 33 (2) ◽  
pp. 606-646 ◽  
Author(s):  
James O. Berger ◽  
William Strawderman ◽  
Dejun Tang
Keyword(s):  
Hierarchical Models ◽  
Posterior Propriety

scholarly journals Posterior Propriety of an Objective Prior in a 4-Level Normal Hierarchical Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Chengyuan Song ◽  
Dongchu Sun ◽  
Kun Fan ◽  
Rongji Mu
Keyword(s):  
Hierarchical Model ◽  
Gibbs Sampler ◽  
Hierarchical Models ◽  
Reference Prior ◽  
Hierarchical Bayesian Models ◽  
Multivariate Normal ◽  
Objective Prior ◽  
Rule Approach ◽  
Posterior Propriety ◽  
Primary Advantage

The use of hierarchical Bayesian models in statistical practice is extensive, yet it is dangerous to implement the Gibbs sampler without checking that the posterior is proper. Formal approaches to objective Bayesian analysis, such as the Jeffreys-rule approach or reference prior approach, are only implementable in simple hierarchical settings. In this paper, we consider a 4-level multivariate normal hierarchical model. We demonstrate the posterior using our recommended prior which is proper in the 4-level normal hierarchical models. A primary advantage of the recommended prior over other proposed objective priors is that it can be used at any level of a hierarchical model.

Hierarchical Models of Mood and Anxiety Disorders and the Role of Temperament

2011 ◽  
Author(s):  
D. Ryan Hooper ◽  
Michael J. Ross ◽  
Jillon S. Vander Wal ◽  
Terri L. Weaver
Keyword(s):  
Anxiety Disorders ◽  
Hierarchical Models ◽  
Mood And Anxiety Disorders

Comparing Hierarchical Models of Personality Pathology

2018 ◽  
Author(s):  
Whitney R. Ringwald ◽  
Aidan G.C. Wright ◽  
Joseph E. Beeney ◽  
Paul A. Pilkonis
Keyword(s):  
Hierarchical Models ◽  
Empirical Work ◽  
Hierarchical Classification ◽  
Personality Pathology ◽  
General Factor ◽  
Factor Analyses ◽  
Conceptual Structures ◽  
Individual Personality ◽  
Exploratory Factor Analyses ◽  
Specific Factors

Two dimensional, hierarchical classification models of personality pathology have emerged as alternatives to traditional categorical systems: multi-tiered models with increasing numbers of factors and models that distinguish between a general factor of severity and specific factors reflecting style. Using a large sample (N=840) with a range of psychopathology, we conducted exploratory factor analyses of individual personality disorder criteria to evaluate the validity of these conceptual structures. We estimated an oblique, “unfolding” hierarchy and a bifactor model, then examined correlations between these and multi-method functioning measures to enrich interpretation. Four-factor solutions for each model, reflecting rotations of each other, fit well and equivalently. The resulting structures are consistent with previous empirical work and provide support for each theoretical model.

Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

2008 ◽  
Author(s):  
Ralph F. Milliff ◽  
Mark Berliner ◽  
Emanuele D. Lorenzo ◽  
Christopher K. Wikle
Keyword(s):  
Hierarchical Models ◽  
Bayesian Hierarchical Models ◽  
Forecast System ◽  
Bayesian Hierarchical ◽  
The Mediterranean

Bayesian Hierarchical Models to Augment the Mediterranean Forecast System

2010 ◽  
Author(s):  
Christopher K. Wikle ◽  
L. M. Berliner ◽  
Emanuele Di Lorenzo ◽  
Ralph F. Milliff
Keyword(s):  
Hierarchical Models ◽  
Bayesian Hierarchical Models ◽  
Forecast System ◽  
Bayesian Hierarchical ◽  
The Mediterranean
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