Generalized structured additive regression based on Bayesian P-splines

Generalized additive models (GAM) for modelling nonlinear effects of continuous covariates are now well established tools for the applied statistician. In this paper we develop Bayesian GAM's and extensions to generalized structured additive regression based on one or two dimensional P-splines as the main building block. The approach extends previous work by Lang und Brezger (2003) for Gaussian responses. Inference relies on Markov chain Monte Carlo (MCMC) simulation techniques, and is either based on iteratively weighted least squares (IWLS) proposals or on latent utility representations of (multi)categorical regression models. Our approach covers the most common univariate response distributions, e.g. the Binomial, Poisson or Gamma distribution, as well as multicategorical responses. For the first time, we present Bayesian semiparametric inference for the widely used multinomial logit models. As we will demonstrate through two applications on the forest health status of trees and a space-time analysis of health insurance data, the approach allows realistic modelling of complex problems. We consider the enormous flexibility and extendability of our approach as a main advantage of Bayesian inference based on MCMC techniques compared to more traditional approaches. Software for the methodology presented in the paper is provided within the public domain package BayesX

Simultaneous probability statements for Bayesian P-splines

P-splines are a popular approach for fitting nonlinear effects of continuous covariates in semiparametric regression models. Recently, a Bayesian version for P-splines has been developed on the basis of Markov chain Monte Carlo simulation techniques for inference. In this work we adopt and generalize the concept of Bayesian contour probabilities to Bayesian P-splines within a generalized additive models framework. More specifically, we aim at computing the maximum credible level (sometimes called Bayesian p-value) for which a particular parameter vector of interest lies within the corresponding highest posterior density (HPD) region. We are particularly interested in parameter vectors that correspond to a constant, linear or more generally a polynomial fit. As an alternative to HPD regions simultaneous credible intervals could be used to define pseudo contour probabilities. Efficient algorithms for computing contour and pseudo contour probabilities are developed. The performance of the approach is assessed through simulation studies and applications to data for the Munich rental guide and on undernutrition in Zambia and Tanzania

BayesX: Analysing Bayesian structured additive regression models

There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This paper describes the capabilities of the public domain software BayesX for estimating complex regression models with structured additive predictor. The program extends the capabilities of existing software for semiparametric regression. Many model classes well known from the literature are special cases of the models supported by BayesX. Examples are Generalized Additive (Mixed) Models, Dynamic Models, Varying Coefficient Models, Geoadditive Models, Geographically Weighted Regression and models for space-time regression. BayesX supports the most common distributions for the response variable. For univariate responses these are Gaussian, Binomial, Poisson, Gamma and negative Binomial. For multicategorical responses, both multinomial logit and probit models for unordered categories of the response as well as cumulative threshold models for ordered categories may be estimated. Moreover, BayesX allows the estimation of complex continuous time survival and hazardrate models

BayesX - Software for Bayesian Inference based on Markov Chain Monte Carlo simulation techniques

BayesX is a Software tool for Bayesian inference based on Markov Chain Monte Carlo (MCMC) inference techniques. The main feature of BayesX so far, is a very powerful regression tool for Bayesian semiparametric regression within the Generalized linear models framework. BayesX is able to estimate nonlinear effects of metrical covariates, trends and flexible seasonal patterns of time scales, structured and/or unstructured random effects of spatial covariates (geographical data) and unstructured random effects of unordered group indicators. Moreover, BayesX is able to estimate varying coefficients models with metrical and even spatial covariates as effectmodifiers. The distribution of the response can be either Gaussian, binomial or Poisson. In addition, BayesX has some useful functions for handling and manipulating datasets and geographical maps

Bayesian P-Splines

P-splines are an attractive approach for modelling nonlinear smooth effects of covariates within the generalized additive and varying coefficient models framework. In this paper we propose a Bayesian version for P-splines and generalize the approach for one dimensional curves to two dimensional surface fitting for modelling interactions between metrical covariates. A Bayesian approach to P-splines has the advantage of allowing for simultaneous estimation of smooth functions and smoothing parameters. Moreover, it can easily be extended to more complex formulations, for example to mixed models with random effects for serially or spatially correlated response. Additionally, the assumption of constant smoothing parameters can be replaced by allowing the smoothing parameters to be locally adaptive. This is particularly useful in situations with changing curvature of the underlying smooth function or where the function is highly oscillating. Inference is fully Bayesian and uses recent MCMC techniques for drawing random samples from the posterior. In a couple of simulation studies the performance of Bayesian P-splines is studied and compared to other approaches in the literature. We illustrate the approach by a complex application on rents for flats in Munich

ρ\rho and K∗K^* resonances on the lattice at nearly physical quark masses and Nf=2N_f=2

Working with a pion mass $m_\pi \approx 150$ MeV, we study $\pi\pi$ and $K\pi$ scattering using two flavours of non-perturbatively improved Wilson fermions at a lattice spacing $a\approx 0.071$ fm. Employing two lattice volumes with linear spatial extents of $N_s=48$ and $N_s=64$ points and moving frames, we extract the phase shifts for p-wave $\pi\pi$ and $K\pi$ scattering near the $\rho$ and $K^*$ resonances.Comparing our results to those of previous lattice studies, that used pion masses ranging from about 200 MeV up to 470 MeV, we find that the coupling $g_{\rho\pi\pi}$ appears to be remarkably constant as a function of $m_{\pi}$.Comment: 16 pages, 8 figures, v2: "and $N_f=2$" added to the title, references updated, some figures replaced, including improved summary plots, alternative parametrizations are considered and analytical continations are performed to determine pole positions on the second Riemann shee

The Error is the Feature: how to Forecast Lightning using a Model Prediction Error

Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed brightness temperatures in different spectral channels and emit a warning if a critical threshold is reached. Recent progress in data science however demonstrates that machine learning can be successfully applied to many research fields in science, especially in areas dealing with large datasets. We therefore present a new approach to the problem of predicting thunderstorms based on machine learning. The core idea of our work is to use the error of two-dimensional optical flow algorithms applied to images of meteorological satellites as a feature for machine learning models. We interpret that optical flow error as an indication of convection potentially leading to thunderstorms and lightning. To factor in spatial proximity we use various manual convolution steps. We also consider effects such as the time of day or the geographic location. We train different tree classifier models as well as a neural network to predict lightning within the next few hours (called nowcasting in meteorology) based on these features. In our evaluation section we compare the predictive power of the different models and the impact of different features on the classification result. Our results show a high accuracy of 96% for predictions over the next 15 minutes which slightly decreases with increasing forecast period but still remains above 83% for forecasts of up to five hours. The high false positive rate of nearly 6% however needs further investigation to allow for an operational use of our approach.Comment: 10 pages, 7 figure

Excited hadrons on the lattice: Mesons

We present results for masses of excited mesons from quenched calculations using chirally improved quarks at pion masses down to 350 MeV. The key features of our analysis are the use of a matrix of correlators from various source and sink operators and a basis which includes quark sources with different spatial widths, thereby improving overlap with states exhibiting radial excitations.Comment: 8 pages, 8 figures; version accepted to PR

Laboratory tests with Lepidoptera to assess non-target effects of Bt maize pollen: analysis of current studies and recommendations for a standardised design

Background and approach Common standards for laboratory studies of non-target organisms are recognised as prerequisite to assist the risk assessments regarding the evaluation of environmental effects of transgenic crops. Here, we provide specific recommendations significant for experimental procedures of laboratory studies to test potential adverse effects of Bt maize on larvae of non-target Lepidoptera. We searched and analysed both ecotoxicological test protocols for pesticides in the EU as well as the non-target tests with Lepidoptera applied in unpublished industry studies submitted officially by agro-companies for the GMO authorisation in Europe. Results The classical ecotoxicology protocols applied for testing pesticides could serve as general guidelines, but do not completely fit the specific and differing requirements for assessing non-target effects of transgenic crops. The analysis of the non-target studies submitted for the application of the cultivation of Bt maize in Europe revealed critical limitations, thus corroborating the urgent need for common quality criteria. Based on our evaluations, we identified several issues requiring harmonisation or standardisation of the experimental conditions and approach, e.g., the application of Bt maize pollen, synthetic toxins, the provided diet for larvae, experimental controls, magnitude and duration of exposure to Bt, relevant variables to be recorded, and sufficient statistical power. Conclusions Our recommendations should stimulate the development of precise guidance for the authorities, and support the operationalisation of the required laboratory tests for the evaluation of non-target effects of Bt maize pollen on non-target Lepidoptera, also contributing to standards of other ecotoxicity tests with Lepidoptera larvae, e.g., for pesticides.This article has been prepared as an outcome of the BfN research project “Basisdaten zur Effektbewertung verschiedener Bt-Toxine auf Schmetterlingslarven” (FKZ 3515890100)

Polarons and slow quantum phonons

We describe the formation and properties of Holstein polarons in the entire parameter regime. Our presentation focuses on the polaron mass and radius, which we obtain with an improved numerical technique. It is based on the combination of variational exact diagonalization with an improved construction of phonon states, providing results even for the strong coupling adiabatic regime. In particular we can describe the formation of large and heavy adiabatic polarons. A comparison of the polaron mass for the one and three dimensional situation explains how the different properties in the static oscillator limit determine the behavior in the adiabatic regime. The transport properties of large and small polarons are characterized by the f-sum rule and the optical conductivity. Our calculations are approximation-free and have negligible numerical error. This allows us to give a conclusive and impartial description of polaron formation. We finally discuss the implications of our results for situations beyond the Holstein model.Comment: Final version, 10 pages, 10 figure