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Hierarchical Bayesian Approach (hierarchical + bayesian_approach)
Selected AbstractsContending with space,time interaction in the spatial prediction of pollution: Vancouver's hourly ambient PM10 fieldENVIRONMETRICS, Issue 5-6 2002Jim Zidek Abstract In this article we describe an approach for predicting average hourly concentrations of ambient PM10 in Vancouver. We know our solution also applies to hourly ozone fields and believe it may be quite generally applicable. We use a hierarchical Bayesian approach. At the primary level we model the logarithmic field as a trend model plus Gaussian stochastic residual. That trend model depends on hourly meteorological predictors and is common to all sites. The stochastic component consists of a 24-hour vector response that we model as a multivariate AR(3) temporal process with common spatial parameters. Removing the trend and AR structure leaves ,whitened' time series of vector series. With this approach (as opposed to using 24 separate univariate time series models), there is little loss of spatial correlation in these residuals compared with that in just the detrended residuals (prior to removing the AR component). Moreover our multivariate approach enables predictions for any given hour to ,borrow strength' through its correlation with adjoining hours. On this basis we develop a spatial predictive distribution for these residuals at unmonitored sites. By transforming the predicted residuals back to the original data scales we can impute Vancouver's hourly PM10 field. Copyright © 2002 John Wiley & Sons, Ltd. [source] Response of recruitment to light availability across a tropical lowland rain forest communityJOURNAL OF ECOLOGY, Issue 6 2009Nadja Rüger Summary 1. ,Many hypotheses about species coexistence involve differential resource use and trade-offs in species' life-history traits. Quantifying resource use across most species in diverse communities, although, has seldom been attempted. 2. ,We use a hierarchical Bayesian approach to quantify the light dependence of recruitment in 263 woody species in a 50-ha long-term forest census plot in Panama. Data on sapling recruitment were obtained using the 1985,1990 and 1990,1995 census intervals. Available light was estimated for each recruit from yearly censuses of canopy density. 3. ,We use a power function (linear log,log relationship) to model the light effect on recruitment. Different responses of recruitment to light are expressed by the light effect parameter b. The distribution of b had a central mode at 0.8, suggesting that recruitment of many species responds nearly linearly to increasing light. 4. ,Nearly every species showed increases in recruitment with increasing light. Just nine species (3%) had recruitment declining with light, while 198 species (75%) showed increasing recruitment in both census intervals. Most of the increases in recruitment were decelerating, i.e. the increase was less at higher light (b < 1). In the remaining species, the response to light varied between census intervals (24 species) or species did not have recruits in both intervals (41 species). 5. ,Synthesis. Nearly all species regenerate better in higher light, and recruitment responses to light are spread along a continuum ranging from modest increase with light to a rather strict requirement for high light. These results support the hypothesis that spatio-temporal variation in light availability may contribute to the diversity of tropical tree species by providing opportunities for niche differentiation with respect to light requirements for regeneration. [source] Forecasting stock prices using a hierarchical Bayesian approachJOURNAL OF FORECASTING, Issue 1 2005Jun Ying Abstract The Ohlson model is evaluated using quarterly data from stocks in the Dow Jones Index. A hierarchical Bayesian approach is developed to simultaneously estimate the unknown coefficients in the time series regression model for each company by pooling information across firms. Both estimation and prediction are carried out by the Markov chain Monte Carlo (MCMC) method. Our empirical results show that our forecast based on the hierarchical Bayes method is generally adequate for future prediction, and improves upon the classical method. Copyright © 2005 John Wiley & Sons, Ltd. [source] Joint projections of temperature and precipitation change from multiple climate models: a hierarchical Bayesian approachJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2009Claudia Tebaldi Summary., Posterior distributions for the joint projections of future temperature and precipitation trends and changes are derived by applying a Bayesian hierachical model to a rich data set of simulated climate from general circulation models. The simulations that are analysed here constitute the future projections on which the Intergovernmental Panel on Climate Change based its recent summary report on the future of our planet's climate, albeit without any sophisticated statistical handling of the data. Here we quantify the uncertainty that is represented by the variable results of the various models and their limited ability to represent the observed climate both at global and at regional scales. We do so in a Bayesian framework, by estimating posterior distributions of the climate change signals in terms of trends or differences between future and current periods, and we fully characterize the uncertain nature of a suite of other parameters, like biases, correlation terms and model-specific precisions. Besides presenting our results in terms of posterior distributions of the climate signals, we offer as an alternative representation of the uncertainties in climate change projections the use of the posterior predictive distribution of a new model's projections. The results from our analysis can find straightforward applications in impact studies, which necessitate not only best guesses but also a full representation of the uncertainty in climate change projections. For water resource and crop models, for example, it is vital to use joint projections of temperature and precipitation to represent the characteristics of future climate best, and our statistical analysis delivers just that. [source] The speed of adjustment of financial ratios: A hierarchical Bayesian approach using mixturesAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2008Pilar Gargallo Abstract This paper presents a hierarchical Bayesian analysis of the partial adjustment model of financial ratios using mixture models, an approach that allows us to estimate the distribution of the adjustment coefficients. More particularly, it enables us to analyse speed of reaction in the presence of shocks affecting financial ratios objectives as a basis to establish homogenous groups of firms. The proposed methodology is illustrated by examining a set of ratios for a sample of firms operating in the U.S. manufacturing sector. Copyright © 2007 John Wiley & Sons, Ltd. 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