scholarly journals Composite likelihood methods for large Bayesian VARs with stochastic volatility

2020 ◽  
Vol 35 (6) ◽  
pp. 692-711
Author(s):  
Joshua C. C. Chan ◽  
Eric Eisenstat ◽  
Chenghan Hou ◽  
Gary Koop
Keyword(s):  
Stochastic Volatility ◽  
Composite Likelihood ◽  
Likelihood Methods ◽  
Bayesian Vars

scholarly journals Composite likelihood methods for histogram-valued random variables

2020 ◽  
Vol 30 (5) ◽  
pp. 1459-1477 ◽  
Author(s):  
T. Whitaker ◽  
B. Beranger ◽  
S. A. Sisson
Keyword(s):  
Random Variables ◽  
Composite Likelihood ◽  
Likelihood Methods

scholarly journals Estimating the relative rate of recombination to mutation in bacteria from single-locus variants using composite likelihood methods

2015 ◽  
Vol 9 (1) ◽  
pp. 200-224 ◽  
Author(s):  
Paul Fearnhead ◽  
Shoukai Yu ◽  
Patrick Biggs ◽  
Barbara Holland ◽  
Nigel French
Keyword(s):  
Relative Rate ◽  
Composite Likelihood ◽  
Single Locus ◽  
Likelihood Methods

scholarly journals On the Marginal Distribution of the Diagonal Blocks in a Blocked Wishart Random Matrix

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Kjetil B. Halvorsen ◽  
Victor Ayala ◽  
Eduardo Fierro
Keyword(s):  
Random Matrix ◽  
Random Fields ◽  
Spatial Interpolation ◽  
Marginal Distribution ◽  
Positive Definite ◽  
Composite Likelihood ◽  
Marginal Density ◽  
Likelihood Methods ◽  
Positive Definite Matrices ◽  
The Matrix

Let A be a (m1+m2)×(m1+m2) blocked Wishart random matrix with diagonal blocks of orders m1×m1 and m2×m2. The goal of the paper is to find the exact marginal distribution of the two diagonal blocks of A. We find an expression for this marginal density involving the matrix-variate generalized hypergeometric function. We became interested in this problem because of an application in spatial interpolation of random fields of positive definite matrices, where this result will be used for parameter estimation, using composite likelihood methods.

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