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Hierarchical Modeling (hierarchical + modeling)
Selected AbstractsBayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology by LAWSON, A. B.BIOMETRICS, Issue 2 2009J. Law No abstract is available for this article. [source] Hierarchical modeling of genome-wide Short Tandem Repeat (STR) markers infers native American prehistoryAMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 2 2010Cecil M. Lewis Jr. Abstract This study examines a genome-wide dataset of 678 Short Tandem Repeat loci characterized in 444 individuals representing 29 Native American populations as well as the Tundra Netsi and Yakut populations from Siberia. Using these data, the study tests four current hypotheses regarding the hierarchical distribution of neutral genetic variation in native South American populations: (1) the western region of South America harbors more variation than the eastern region of South America, (2) Central American and western South American populations cluster exclusively, (3) populations speaking the Chibchan-Paezan and Equatorial-Tucanoan language stock emerge as a group within an otherwise South American clade, (4) Chibchan-Paezan populations in Central America emerge together at the tips of the Chibchan-Paezan cluster. This study finds that hierarchical models with the best fit place Central American populations, and populations speaking the Chibchan-Paezan language stock, at a basal position or separated from the South American group, which is more consistent with a serial founder effect into South America than that previously described. Western (Andean) South America is found to harbor similar levels of variation as eastern (Equatorial-Tucanoan and Ge-Pano-Carib) South America, which is inconsistent with an initial west coast migration into South America. Moreover, in all relevant models, the estimates of genetic diversity within geographic regions suggest a major bottleneck or founder effect occurring within the North American subcontinent, before the peopling of Central and South America. Am J Phys Anthropol 2010. © 2009 Wiley-Liss, Inc. [source] DIRECTLY INTERVENE OR CALL THE AUTHORITIES?CRIMINOLOGY, Issue 1 2007A STUDY OF FORMS OF NEIGHBORHOOD SOCIAL CONTROL WITHIN A SOCIAL DISORGANIZATION FRAMEWORK Informal social control is a central concept in the contemporary social disorganization literature, and much attention has been directed at examining community characteristics related to variation in the quantity of informal social control across communities. However, considerably less attention has been paid to variation in forms of informal social control. This study examines the extent to which neighborhood characteristics are related to residents'likelihood of using two different forms of informal social control: direct informal social control (i.e., through direct intervention) and indirect informal social control (i.e., through mobilizing formal authorities). Data for this study are based on surveys of residents in 66 neighborhoods. The analysis uses hierarchical modeling to examine whether neighborhood characteristics central to contemporary social disorganization theory have similar effects on these two forms of neighborhood social control. Findings indicate that social ties increase the likelihood of direct informal social control but not indirect informal social control, whereas social cohesion and trust decreases indirect informal social control but does not have a significant effect on direct informal social control. Faith in the police is not found to affect either form of informal social control. These findings are discussed in terms of current issues in contemporary social disorganization theory. [source] Assessing ecosystem threats from global and regional change: hierarchical modeling of risk to sagebrush ecosystems from climate change, land use and invasive species in Nevada, USAECOGRAPHY, Issue 1 2010Bethany A. Bradley Global change poses significant challenges for ecosystem conservation. At regional scales, climate change may lead to extensive shifts in species distributions and widespread extirpations or extinctions. At landscape scales, land use and invasive species disrupt ecosystem function and reduce species richness. However, a lack of spatially explicit models of risk to ecosystems makes it difficult for science to inform conservation planning and land management. Here, I model risk to sagebrush (Artemisia spp.) ecosystems in the state of Nevada, USA from climate change, land use/land cover change, and species invasion. Risk from climate change is based on an ensemble of 10 atmosphere-ocean general circulation model (AOGCM) projections applied to two bioclimatic envelope models (Mahalanobis distance and Maxent). Risk from land use is based on the distribution of roads, agriculture, and powerlines, and on the spatial relationships between land use and probability of cheatgrass Bromus tectorum invasion in Nevada. Risk from land cover change is based on probability and extent of pinyon-juniper (Pinus monophylla; Juniperus spp.) woodland expansion. Climate change is most likely to negatively impact sagebrush ecosystems at the edges of its current range, particularly in southern Nevada, southern Utah, and eastern Washington. Risk from land use and woodland expansion is pervasive throughout Nevada, while cheatgrass invasion is most problematic in the northern part of the state. Cumulatively, these changes pose major challenges for conservation of sagebrush and sagebrush obligate species. This type of comprehensive assessment of ecosystem risk provides managers with spatially explicit tools important for conservation planning. [source] Empirical Bayes estimators and non-parametric mixture models for space and time,space disease mapping and surveillanceENVIRONMETRICS, Issue 5 2003Dankmar Böhning Abstract The analysis of the geographic variation of disease and its representation on a map is an important topic in epidemiological research and in public health in general. Identification of spatial heterogeneity of relative risk using morbidity and mortality data is required. Frequently, interest is also in the analysis of space data with respect to time, where typically data are used which are aggregated in certain time windows like 5 or 10 years. The occurrence measure of interest is usually the standardized mortality (morbidity) ratio (SMR). It is well known that disease maps in space or in space and time should not solely be based upon the crude SMR but rather some smoothed version of it. This fact has led to a tremendous amount of theoretical developments in spatial methodology, in particular in the area of hierarchical modeling in connection with fully Bayesian estimation techniques like Markov chain Monte Carlo. It seems, however, that at the same time, where these theoretical developments took place, on the practical side only very few of these developments have found their way into daily practice of epidemiological work and surveillance routines. In this article we focus on developments that avoid the pitfalls of the crude SMR and simultaneously retain a simplicity and, at least approximately, the validity of more complex models. After an illustration of the typical pitfalls of the crude SMR the article is centered around three issues: (a) the separation of spatial random variation from spatial structural variation; (b) a simple mixture model for capturing spatial heterogeneity; (c) an extension of this model for capturing temporal information. The techniques are illustrated by numerous examples. Public domain software like Dismap is mentioned that enables easy mixture modeling in the context of disease mapping. Copyright © 2003 John Wiley & Sons, Ltd. [source] Bayesian hierarchical generalized linear models for a geographical subset of recovery dataENVIRONMETRICS, Issue 2 2002Daniela Cocchi Abstract The aim of this work is to check whether modifications in the length of the hunting seasons had an effect on the chance of reproduction of different species of ringed birds. We start from a national data set of ringing-recovered data on three species of game birds. Only data on birds recovered as juveniles are used. Data on recoveries are organized in a 4-way contingency table. Several generalized linear models are proposed for the counts of recovered birds. Bayesian hierarchical modeling is particularly suitable for this kind of data, for which an over-dispersion parameter can be introduced at the second level of the hierarchy. Maximum Likelihood and Bayesian solutions are computed for the different models: the Bayesian framework, in particular under an individual modeling of over-dispersion, exhibits the best fit in terms of Bayesian p -value. The results show that the modification in the length of the hunting seasons does not produce equal benefits for the three species considered. Copyright © 2002 John Wiley & Sons, Ltd. [source] Predicting Patterns of Mammography Use: A Geographic Perspective on National Needs for Intervention ResearchHEALTH SERVICES RESEARCH, Issue 4 2002Julie Legler Objective. To introduce a methodology for planning preventive health service research that takes into account geographic context. Data Sources. National Health Interview Survey (NHIS) self-reports of mammography within the past two years, 1987, and 1993,94. Area Resource File (ARF), 1990. Database of mammography intervention research studies conducted from 1984 to 1994. Design. Bayesian hierarchical modeling describes mammography as a function of county-level socioeconomic data and explicitly estimates the geographic variation unexplained by the county-level data. This model produces county use estimates (both NHIS-sampled and unsampled), which are aggregated for entire states. The locations of intervention research studies are examined in light of the statewide mammography utilization estimates. Data Extraction. Individual level NHIS data were merged with county-level data from the ARF. Principal Findings. State maps reveal the estimated distribution of mammography utilization and intervention research. Eighteen states with low mammography use reported no intervention research activity. County-level occupation and education were important predictors for younger women in 1993,94. In 1987, they were not predictive for any demographic group. Conclusions. Opportunities exist to improve the planning of future intervention research by considering geographic context. Modeling results suggest that the choice of predictors be tailored to both the population and the time period under study when planning interventions. [source] Human DNA sequences: More variation and less raceAMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 1 2009Jeffrey C. Long Abstract Interest in genetic diversity within and between human populations as a way to answer questions about race has intensified in light of recent advances in genome technology. The purpose of this article is to apply a method of generalized hierarchical modeling to two DNA data sets. The first data set consists of a small sample of individuals (n = 32 total, from eight populations) who have been fully resequenced for 63 loci that encode a total of 38,534 base pairs. The second data set consists of a large sample of individuals (n = 928 total, from 46 populations) who have been genotyped at 580 loci that encode short tandem repeats. The results are clear and somewhat surprising. We see that populations differ in the amount of diversity that they harbor. The pattern of DNA diversity is one of nested subsets, such that the diversity in non-Sub-Saharan African populations is essentially a subset of the diversity found in Sub-Saharan African populations. The actual pattern of DNA diversity creates some unsettling problems for using race as meaningful genetic categories. For example, the pattern of DNA diversity implies that some populations belong to more than one race (e.g., Europeans), whereas other populations do not belong to any race at all (e.g., Sub-Saharan Africans). As Frank Livingstone noted long ago, the Linnean classification system cannot accommodate this pattern because within the system a population cannot belong to more than one named group within a taxonomic level. Am J Phys Anthropol 2009. © 2009 Wiley-Liss, Inc. [source] Bayesian nonparametric hierarchical modelingBIOMETRICAL JOURNAL, Issue 2 2009David B. Dunson Abstract In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods. [source] | |||||||||||||