Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods

Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions.A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the ARDS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. The empirical examples include a regression model with scale contamination and a mixture model for economic growth of the USA.Markov chain Monte Carlo;importance sampling;radial coordinates

Explaining Adaptive Radial-Based Direction Sampling

In this short paper we summarize the computational steps of Adaptive Radial-Based Direction Sampling (ARDS), which can be used for Bayesian analysis of ill behaved target densities. We consider one simulation experiment in order to illustrate the good performance of ARDS relative to the independence chain MH algorithm and importance sampling.importance sampling;Markov Chain Monte Carlo;radial coordinates

Explaining Adaptive Radial-Based Direction Sampling

In this short paper we summarize the computational steps of Adaptive Radial-Based Direction Sampling (ARDS), which can be used for Bayesian analysis of ill behaved target densities. We consider one simulation experiment in order to illustrate the good performance of ARDS relative to the independence chain MH algorithm and importance sampling

Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods

Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to sample directions and, conditionally on these, distances are generated by inverting the cumulative distribution function. A sequential procedure is applied to update the initial location and scaling matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the APS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. APS is applied to several econometric and statistical examples. The empirical results for a regression model with scale contamination, an ARMA-GARCH-Student t model with near cancellation of roots and heavy tails, a mixture model for economic growth, and a nonlinear threshold model for industrial production growth confirm the practical flexibility and robustness of APS

A nonlocal, covariant generalisation of the NJL model

We solve a nonlocal generalisation of the NJL model in the Hartree approximation. This model has a separable interaction, as suggested by instanton models of the QCD vacuum. The choice of form factor in this interaction is motivated by the confining nature of the vacuum. A conserved axial current is constructed in the chiral limit of the model and the pion properties are shown to satisfy the Gell-Mann--Oakes--Renner relation. For reasonable values of the parameters the model exhibits quark confinement.Comment: 13 pages (RevTeX), MC/TH 94/1

Hindsight bias and outcome bias in judging directors’ liability and the role of free will beliefs

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The arrhythmogenic cardiomyopathy phenotype associated with PKP2 c.1211dup variant

Background: The arrhythmogenic cardiomyopathy (ACM) phenotype, with life-threatening ventricular arrhythmias and heart failure, varies according to genetic aetiology. We aimed to characterise the phenotype associated with the variant c.1211dup (p.Val406Serfs*4) in the plakophilin‑2 gene (PKP2) and compare it with previously reported Dutch PKP2 founder variants. Methods: Clinical data were collected retrospectively from medical records of 106 PKP2 c.1211dup heterozygous carriers. Using data from the Netherlands ACM Registry, c.1211dup was compared with 3 other truncating PKP2 variants (c.235C &gt; T (p.Arg79*), c.397C &gt; T (p.Gln133*) and c.2489+1G &gt; A (p.?)). Results: Of the 106 carriers, 47 (44%) were diagnosed with ACM, at a mean age of 41 years. By the end of follow-up, 29 (27%) had experienced sustained ventricular arrhythmias and 12 (11%) had developed heart failure, with male carriers showing significantly higher risks than females on these endpoints (p &lt; 0.05). Based on available cardiac magnetic resonance imaging and echocardiographic data, 46% of the carriers showed either right ventricular dilatation and/or dysfunction, whereas a substantial minority (37%) had some form of left ventricular involvement. Both geographical distribution of carriers and haplotype analysis suggested PKP2 c.1211dup to be a founder variant originating from the South-Western coast of the Netherlands. Finally, a Cox proportional hazards model suggested significant differences in ventricular arrhythmia–free survival between 4 PKP2 founder variants, including c.1211dup. Conclusions: The PKP2 c.1211dup variant is a Dutch founder variant associated with a typical right-dominant ACM phenotype, but also left ventricular involvement, and a possibly more severe phenotype than other Dutch PKP2 founder variants.</p

Measurement of the p-pbar -> Wgamma + X cross section at sqrt(s) = 1.96 TeV and WWgamma anomalous coupling limits

The WWgamma triple gauge boson coupling parameters are studied using p-pbar -> l nu gamma + X (l = e,mu) events at sqrt(s) = 1.96 TeV. The data were collected with the DO detector from an integrated luminosity of 162 pb^{-1} delivered by the Fermilab Tevatron Collider. The cross section times branching fraction for p-pbar -> W(gamma) + X -> l nu gamma + X with E_T^{gamma} > 8 GeV and Delta R_{l gamma} > 0.7 is 14.8 +/- 1.6 (stat) +/- 1.0 (syst) +/- 1.0 (lum) pb. The one-dimensional 95% confidence level limits on anomalous couplings are -0.88 < Delta kappa_{gamma} < 0.96 and -0.20 < lambda_{gamma} < 0.20.Comment: Submitted to Phys. Rev. D Rapid Communication

Measurement of the ttbar Production Cross Section in ppbar Collisions at sqrt{s} = 1.96 TeV using Kinematic Characteristics of Lepton + Jets Events

We present a measurement of the top quark pair ttbar production cross section in ppbar collisions at a center-of-mass energy of 1.96 TeV using 230 pb**{-1} of data collected by the DO detector at the Fermilab Tevatron Collider. We select events with one charged lepton (electron or muon), large missing transverse energy, and at least four jets, and extract the ttbar content of the sample based on the kinematic characteristics of the events. For a top quark mass of 175 GeV, we measure sigma(ttbar) = 6.7 {+1.4-1.3} (stat) {+1.6- 1.1} (syst) +/-0.4 (lumi) pb, in good agreement with the standard model prediction.Comment: submitted to Phys.Rev.Let

Measurement of the ttbar Production Cross Section in ppbar Collisions at sqrt(s)=1.96 TeV using Lepton + Jets Events with Lifetime b-tagging

We present a measurement of the top quark pair ($t\bar{t}$) production cross section ($\sigma_{t\bar{t}}$) in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV using 230 pb$^{-1}$ of data collected by the D0 experiment at the Fermilab Tevatron Collider. We select events with one charged lepton (electron or muon), missing transverse energy, and jets in the final state. We employ lifetime-based b-jet identification techniques to further enhance the $t\bar{t}$ purity of the selected sample. For a top quark mass of 175 GeV, we measure $\sigma_{t\bar{t}}=8.6^{+1.6}_{-1.5}(stat.+syst.)\pm 0.6(lumi.)$ pb, in agreement with the standard model expectation.Comment: 7 pages, 2 figures, 3 tables Submitted to Phys.Rev.Let