1,721,208 research outputs found
Exploratory lattice QCD studies of rare kaon decays
No full textThe rare kaon decays K → πl+l- and K → πνν proceed via flavour changing neutral currents, and are thus heavily suppressed in the Standard Model. This natural suppression makes these decays sensitive to the effects of potential new physics. These decays first arise as second-order electroweak processes, hence we are required to evaluate four-point correlation functions involving two effective operators. The evaluation of such four-point correlation functions presents two key difficulties: the appearance of unphysical terms in Euclidean-space correlators that grow exponentially as the operators are separated, and the presence of ultra-violet divergences as the operators approach each other. I present the results of the first exploratory studies of the calculation of the long-distance contributions to these decays using lattice QCD. The decays K → πl+l- are completely long-distance dominated; this lattice calculation is thus the first step in providing ab-initio estimates for the amplitudes of these decays. Our simulations are performed using the 243 x 64 domain wall fermion ensemble of the RBC-UKQCD collaboration, with a pion mass of 430(2)MeV, a kaon mass of 625(2)MeV, and a valence charm mass of 543(13)MeV. In particular we determine the form factor, V (z), of the K+ (k) → π+ (p) l+l- decay from the lattice at small values of z = q2=M2K (where q = k - p), obtaining V (z) = 1.37(36); 0.68(39); 0.96(64) for the three values of z = -0.5594(12), -1.0530(34), -1.4653(82) respectively.The decays K+ → π+νν are short-distance dominated, although the long-distance contributions represent significant sources of uncertainty. The lattice calculation of the decay amplitudes is made particularly difficult by the presence of ultra-violet divergences in the four-point correlators. I present the calculation of the renormalised decay amplitudes, using the 163x32 domain wall fermion ensemble of the RBC-UKQCD collaboration, with a pion mass of 421(1)(7)MeV, a kaon mass of 563(1)(9)MeV, and a valence charm mass of 863(24)MeV. In particular we find the difference between the perturbative and lattice estimates of the charm contribution to these decays to be ΔPc = 0.0040(13)(32)(-45)
Bayesian multi-scale modeling for aggregated disease mapping data
No full textIn disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.The authors would like to acknowledge support from the National Institutes of Health via grant
R01CA172805. The third author also acknowledges support from the IAP Research Network P7/06 of the
Belgian State (Belgian Science Policy)
Assessing the impact of neighborhood structures in Bayesian disease mapping
No full textIn Bayesian disease mapping, defining the neighborhood structure is crucial when fitting the conditional auto-regressive model. Yet, there has been little assessment of how different structures affect the model performance in case of fine-scale data. This paper explores this gap. In a case study examining COVID-19 pandemic effects, 2020 mortality is contrasted with pre-pandemic rates in small areas in Limburg (Belgium). Data are modeled using BYM and BYM2, with three broadening queen-neighborhood structures up to the fifth-order neighbors and two weight schemes. A simulation study assesses model performance in reproducing the pairwise spatial correlation at different neighbor orders. Models are compared regarding WAIC, goodness-of-fit, parameter estimates, and computation time. Results show that the order-based weight matrix performs better than the binary matrix. The simple first-order neighborhood structure shows comparable performance to larger higher-order structures while requiring much less computation time. The BYM model is more impacted by the choice of the neighborhood as compared to the BYM2 model. Our findings suggest minimal advantages in employing higher-order neighborhood matrices. In conclusion, our study indicates that opting for a simple first-order neighborhood structure is a pragmatic and suitable choice when applying a conditional auto-regressive model to fine-scale data in Bayesian disease mapping.TN gratefully acknowledges financial support from the Research Foundation - Flanders [grant number G0A4121N]
Spatially-dependent Bayesian Model Selection for Disease Mapping
Full text linkIn disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found
Multiscale Measurement Error Models for Aggregated Small Area Health Data
No full textSpatial data are often aggregated from a finer (smaller) to a coarser (larger) geographical level. The process of data aggregation induces a scaling effect which smoothes the variation in the data. To address the scaling problem, multiscale models that link the convolution models at different scale levels via the shared random effect have been proposed. One of the main goals in aggregated health data is to investigate the relationship between predictors and an outcome at different geographical levels. In this paper, we extend multiscale models to examine whether a predictor effect at a finer level hold true at a coarser level. To adjust for predictor uncertainty due to aggregation, we applied measurement error models in the framework of multiscale approach. To assess the benefit of using multiscale measurement error models, we compare the performance of multiscale models with and without measurement error in both real and simulated data. We found that ignoring the measurement error in multiscale models underestimates the regression coefficient, while it overestimates the variance of the spatially structured random effect. On the other hand, accounting for the measurement error in multiscale models provides a better model fit and unbiased parameter estimates
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
The bivariate combined model for spatial data analysis
No full textTo describe the spatial distribution of diseases, a number of methods have been proposed to model relative risks within areas. Most models use Bayesian hierarchical methods, in which one models both spatially structured and unstructured extra-Poisson variance present in the data. For modelling a single disease, the conditional autoregressive (CAR) convolution model has been very popular. More recently, a combined model was proposed that ‘combines’ ideas from the CAR convolution model and the well-known Poisson-gamma model. The combined model was shown to be a good alternative to the CAR convolution model when there was a large amount of uncorrelated extra-variance in the data. Less solutions exist for modelling two diseases simultaneously or modelling a disease in two sub-populations simultaneously. Furthermore, existing models are typically based on the CAR convolution model. In this paper, a bivariate version of the combined model is proposed in which the unstructured heterogeneity term is split up into terms that are shared and terms that are specific to the disease or subpopulation, while spatial dependency is introduced via a univariate or multivariate Markov random field. The proposed method is illustrated by analysis of disease data in Georgia (USA) and Limburg (Belgium) and in a simulation study. We conclude that the bivariate combined model constitutes an interesting model when two diseases are possibly correlated. As the choice of the preferred model differs between data sets, we suggest to use the new and existing modelling approaches together and to choose the best model via goodness-of-fit statistics. Copyright © 2016 John Wiley & Sons, Ltd
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