878 resultados para Hierarchical Bayesian Metaanalysis
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Apart from common cases of differential argument marking, referential hierarchies affect argument marking in two ways: (a) through hierarchical marking, where markers compete for a slot and the competition is resolved by a hierarchy, and (b) through co-argument sensitivity, where the marking of one argument depends on the properties of its co-argument. Here we show that while co-argument sensitivity cannot be analyzed in terms of hierarchical marking, hierarchical marking can be analyzed in terms of co-argument sensitivity. Once hierarchical effects on marking are analyzed in terms of co-argument sensitivity, it becomes possible to examine alignment patterns relative to referential categories in exactly the same way as one can examine alignment patterns relative to referential categories in cases of differential argument marking and indeed any other condition on alignment (such as tense or clause type). As a result, instances of hierarchical marking of any kind turn out not to present a special case in the typology of alignment, and there is no need for positing an additional non-basic alignment type such as “hierarchical alignment”. While hierarchies are not needed for descriptive and comparative purposes, we also cast doubt on their relevance in diachrony: examining two families for which hierarchical agreement has been postulated, Algonquian and Kiranti, we find only weak and very limited statistical evidence for agreement paradigms to have been shaped by a principled ranking of person categories.
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Mode of access: Internet.
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Item 247.
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"This report reproduces a thesis of the same title submitted to the Department of Electrical Engineering, Massachusetts Institute of Technology, in partial fulfillment of the requirements for the degree of Doctor of Philosophy, May 1970."--p. 2
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A reply to Bishop John Hughes' "A lecture on the mixture of civil and ecclesiastical power, in the governments of the Middle Ages."
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Performing organization: Dept. of Statistics, University of Michigan.
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Performing organization: Dept. of Statistics, University of Michigan.
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"Performing organization: Oklahoma State University, College of Business Administration , Stillwater."
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Thesis (Ph.D.)--University of Washington, 2016-08
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Thesis (Ph.D.)--University of Washington, 2016-06
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The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.
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The founding of new populations by small numbers of colonists has been considered a potentially important mechanism promoting evolutionary change in island populations. Colonizing species, such as members of the avian species complex Zosterops lateralis, have been used to support this idea. A large amount of background information on recent colonization history is available for one Zosterops subspecies, Z. lateralis lateralis, providing the opportunity to reconstruct the population dynamics of its colonization sequence. We used a Bayesian approach to combine historical and demographic information available on Z. l. lateralis with genotypic data from six microsatellite loci, and a rejection algorithm to make simultaneous inferences on the demographic parameters describing the recent colonization history of this subspecies in four southwest Pacific islands. Demographic models assuming mutation–drift equilibrium or a large number of founders were better supported than models assuming founder events for three of four recently colonized island populations. Posterior distributions of demographic parameters supported (i) a large stable effective population size of several thousands individuals with point estimates around 4000–5000; (ii) a founder event of very low intensity with a large effective number of founders around 150–200 individuals for each island in three of four islands, suggesting the colonization of those islands by one flock of large size or several flocks of average size; and (iii) a founder event of higher intensity on Norfolk Island with an effective number of founders around 20 individuals, suggesting colonization by a single flock of moderate size. Our inferences on demographic parameters, especially those on the number of founders, were relatively insensitive to the precise choice of prior distributions for microsatellite mutation processes and demographic parameters, suggesting that our analysis provides a robust description of the recent colonization history of the subspecies.
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We investigate whether relative contributions of genetic and shared environmental factors are associated with an increased risk in melanoma. Data from the Queensland Familial Melanoma Project comprising 15,907 subjects arising from 1912 families were analyzed to estimate the additive genetic, common and unique environmental contributions to variation in the age at onset of melanoma. Two complementary approaches for analyzing correlated time-to-onset family data were considered: the generalized estimating equations (GEE) method in which one can estimate relationship-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modeled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov Chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the free ware package BUGS. In addition, we also used a Bayesian model to investigate the relative contribution of genetic and environmental effects on the expression of naevi and freckles, which are known risk factors for melanoma.
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Background: The aim of this article was to investigate the size and possible causes of the reported excess in coronary events on Mondays. Methods: We conducted a metaanalysis of data from the World Health Organization (WHO) MONICA Project, which monitored trends and determinants in cardiovascular disease. The MONICA Project was undertaken in 21 countries from 1980 to 1995. Results: We found a small overall excess rate of coronary events on Mondays. In a population experiencing 100 events per week, we estimate there would be approximately I more event on Monday than on any other day. Hierarchical logistic regression showed that the Monday excess was greater in centers with less thorough data collection procedures. Conclusions: The excess of coronary events on Mondays is probably an artifact resulting from events with uncertain dates being coded as taking place on Mondays.