Bayesian forecasting and scalable multivariate volatility analysis using simultaneous graphical dynamic models

The recently introduced class of simultaneous graphical dynamic linear models (SGDLMs) defines an ability to scale on-line Bayesian analysis and forecasting to higher-dimensional time series. This paper advances the methodology of SGDLMs, developing and embedding a novel, adaptive method of simultaneous predictor selection in forward filtering for on-line learning and forecasting. The advances include developments in Bayesian computation for scalability, and a case study in exploring the resulting potential for improved short-term forecasting of large-scale volatility matrices. A case study concerns financial forecasting and portfolio optimization with a 400-dimensional series of daily stock prices. Analysis shows that the SGDLM forecasts volatilities and co-volatilities well, making it ideally suited to contributing to quantitative investment strategies to improve portfolio returns. We also identify performance metrics linked to the sequential Bayesian filtering analysis that turn out to define a leading indicator of increased financial market stresses, comparable to but leading the standard St. Louis Fed Financial Stress Index (STLFSI) measure. Parallel computation using GPU implementations substantially advance the ability to fit and use these models.Comment: 28 pages, 9 figures, 7 table

Bayesian Model Selection of Regular Vine Copulas

Regular vine copulas are a flexible class of dependence models, but Bayesian methodology for model selection and inference is not yet fully developed. We propose sparsity-inducing but otherwise non-informative priors, and present novel proposals to enable reversible jump Markov chain Monte Carlo posterior simulation for Bayesian model selection and inference. Our method is the first to jointly estimate the posterior distribution of all trees of a regular vine copula. This represents a substantial improvement over existing frequentist and Bayesian strategies, which can only select one tree at a time and are known to induce bias. A simulation study demonstrates the feasibility of our strategy and shows that it combines superior selection and reduced computation time compared to Bayesian tree-by-tree selection. In a real data example, we forecast the daily expected tail loss of a portfolio of nine exchange-traded funds using a fully Bayesian multivariate dynamic model built around Bayesian regular vine copulas to illustrate our model’s viability for financial analysis and risk estimation

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

Estimating the Use of Public Lands: Integrated Modeling of Open Populations with Convolution Likelihood Ecological Abundance Regression

We present an integrated open population model where the population dynamics are defined by a differential equation, and the related statistical model utilizes a Poisson binomial convolution likelihood. Key advantages of the proposed approach over existing open population models include the flexibility to predict related, but unobserved quantities such as total immigration or emigration over a specified time period, and more computationally efficient posterior simulation by elimination of the need to explicitly simulate latent immigration and emigration. The viability of the proposed method is shown in an in-depth analysis of outdoor recreation participation on public lands, where the surveyed populations changed rapidly and demographic population closure cannot be assumed even within a single day

A Bayesian method for assessing multi-scale species-habitat relationships

Context Scientists face several theoretical and methodological challenges in appropriately describing fundamental wildlife-habitat relationships in models. The spatial scales of habitat relationships are often unknown, and are expected to follow a multi-scale hierarchy. Typical frequentist or information theoretic approaches often suffer under collinearity in multiscale studies, fail to converge when models are complex or represent an intractable computational burden when candidate model sets are large. Objectives Our objective was to implement an automated, Bayesian method for inference on the spatial scales of habitat variables that best predict animal abundance. Methods We introduce Bayesian latent indicator scale selection (BLISS), a Bayesian method to select spatial scales of predictors using latent scale indicator variables that are estimated with reversible-jump Markov chain Monte Carlo sampling. BLISS does not suffer from collinearity, and substantially reduces computation time of studies. We present a simulation study to validate our method and apply our method to a case-study of land cover predictors for ring-necked pheasant (Phasianus colchicus) abundance in Nebraska, USA. Results Our method returns accurate descriptions of the explanatory power of multiple spatial scales, and unbiased and precise parameter estimates under commonly encountered data limitations including spatial scale autocorrelation, effect size, and sample size. BLISS outperforms commonly used model selection methods including stepwise and AIC, and reduces runtime by 90%. Conclusions Given the pervasiveness of scale-dependency in ecology, and the implications of mismatches between the scales of analyses and ecological processes, identifying the spatial scales over which species are integrating habitat information is an important step in understanding species-habitat relationships. BLISS is a widely applicable method for identifying important spatial scales, propagating scale uncertainty, and testing hypotheses of scaling relationships

Insulin Glargine in the Intensive Care Unit: A Model-Based Clinical Trial Design

Online 4 Oct 2012Introduction: Current succesful AGC (Accurate Glycemic Control) protocols require extra clinical effort and are impractical in less acute wards where patients are still susceptible to stress-induced hyperglycemia. Long-acting insulin Glargine has the potential to be used in a low effort controller. However, potential variability in efficacy and length of action, prevent direct in-hospital use in an AGC framework for less acute wards. Method: Clinically validated virtual trials based on data from stable ICU patients from the SPRINT cohort who would be transferred to such an approach are used to develop a 24-hour AGC protocol robust to different Glargine potencies (1.0x, 1.5x and 2.0x regular insulin) and initial dose sizes (dose = total insulin over prior 12, 18 and 24 hours). Glycemic control in this period is provided only by varying nutritional inputs. Performance is assessed as %BG in the 4.0-8.0mmol/L band and safety by %BG<4.0mmol/L. Results: The final protocol consisted of Glargine bolus size equal to insulin over the previous 18 hours. Compared to SPRINT there was a 6.9% - 9.5% absolute decrease in mild hypoglycemia (%BG<4.0mmol/L) and up to a 6.2% increase in %BG between 4.0 and 8.0mmol/L. When the efficacy is known (1.5x assumed) there were reductions of: 27% BG measurements, 59% insulin boluses, 67% nutrition changes, and 6.3% absolute in mild hypoglycemia. Conclusion: A robust 24-48 clinical trial has been designed to safely investigate the efficacy and kinetics of Glargine as a first step towards developing a Glargine-based protocol for less acute wards. Ensuring robustness to variability in Glargine efficacy significantly affects the performance and safety that can be obtained

Results and perspectives of the solar axion search with the CAST experiment

The status of the solar axion search with the CERN Axion Solar Telescope (CAST) will be presented. Recent results obtained by the use of $^3$He as a buffer gas has allowed us to extend our sensitivity to higher axion masses than our previous measurements with $^4$He. With about 1 h of data taking at each of 252 different pressure settings we have scanned the axion mass range 0.39 eV$\le m_{a} \le$ 0.64 eV. From the absence of an excess of x rays when the magnet was pointing to the Sun we set a typical upper limit on the axion-photon coupling of g$_{a\gamma} \le 2.3\times 10^{-10}$ GeV$^{-1}$ at 95% C.L., the exact value depending on the pressure setting. CAST published results represent the best experimental limit on the photon couplings to axions and other similar exotic particles dubbed WISPs (Weakly Interacting Slim Particles) in the considered mass range and for the first time the limit enters the region favored by QCD axion models. Preliminary sensitivities for axion masses up to 1.16 eV will also be shown reaching mean upper limits on the axion-photon coupling of g$_{a\gamma} \le 3.5\times 10^{-10}$ GeV$^{-1}$ at 95% C.L. Expected sensibilities for the extension of the CAST program up to 2014 will be presented. Moreover long term options for a new helioscope experiment will be evoked.Comment: 4 pages, 2 pages, to appear in the proceedings of the 24th Rencontres de Blois V2 A few affiliations were not corrected in previous version V3 Author adde

The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts

© 2015 International Society for Microbial Ecology All rights reserved. Despite being one of the simplest metazoans, corals harbor some of the most highly diverse and abundant microbial communities. Differentiating core, symbiotic bacteria from this diverse hostassociated consortium is essential for characterizing the functional contributions of bacteria but has not been possible yet. Here we characterize the coral core microbiome and demonstrate clear phylogenetic and functional divisions between the micro-scale, niche habitats within the coral host. In doing so, we discover seven distinct bacterial phylotypes that are universal to the core microbiome of coral species, separated by thousands of kilometres of oceans. The two most abundant phylotypes are co-localized specifically with the corals' endosymbiotic algae and symbiont-containing host cells. These bacterial symbioses likely facilitate the success of the dinoflagellate endosymbiosis with corals in diverse environmental regimes