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  • Blocking  (1)
  • Familial progressive cardiac conduction defect  (1)
  • 1
    ISSN: 1432-1971
    Keywords: Syncope ; Left bundle-branch block ; Complete heart block ; Cardiomyopathy ; Familial progressive cardiac conduction defect
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Summary Three siblings evaluated for syncope were found to have left bundle-branch block. Progression to complete heart block occurred in all. Pacemaker implantation eliminated syncopal episodes in each case. Echocardiographic manifestations of cardiomyopathy were present in each child despite normal roentgenographic heart size. Careful family study identified no other affected members with conduction defects. These children are believed to represent a form of familial cardiomyopathy in which clinical manifestations of cardiac conduction system disease predominate.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Statistics and computing 9 (1999), S. 17-26 
    ISSN: 1573-1375
    Keywords: Blocking ; correlated binary data ; convergence acceleration ; Gibbs sampler ; Metropolis-Hastings algorithm ; linear mixed model ; panel data ; random effects
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Markov chain Monte Carlo (MCMC) algorithms have revolutionized Bayesian practice. In their simplest form (i.e., when parameters are updated one at a time) they are, however, often slow to converge when applied to high-dimensional statistical models. A remedy for this problem is to block the parameters into groups, which are then updated simultaneously using either a Gibbs or Metropolis-Hastings step. In this paper we construct several (partially and fully blocked) MCMC algorithms for minimizing the autocorrelation in MCMC samples arising from important classes of longitudinal data models. We exploit an identity used by Chib (1995) in the context of Bayes factor computation to show how the parameters in a general linear mixed model may be updated in a single block, improving convergence and producing essentially independent draws from the posterior of the parameters of interest. We also investigate the value of blocking in non-Gaussian mixed models, as well as in a class of binary response data longitudinal models. We illustrate the approaches in detail with three real-data examples.
    Type of Medium: Electronic Resource
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