Silk Investment in Gifts By Males of the Nuptial Feeding Spider Pisaura Mirabilis (Araneae: Pisauridae).

Adult males of the hunting spider Pisaura mirabilis wrap up prey with silk and pass these nuptial gifts to females prior to copulation. The females digest the nuptial gifts, including the silk, during mating. Laboratory experiments were carried out to determine the amount of silk males of P. mirabilis invest in nuptial gifts, and its possible role in sexual reproduction. The amount of silk was always small, indicating that the silk of the nuptial gift has little nutritional value for females. Males that had more time to wrap up the prey produced a larger amount of silk. Starved males required more time than satiated males to produce a given amount of silk. A larger male body size had a positive effect on the amount of silk. In general, the size of the prey used for nuptial gifts had no influence on the amount of silk. However, due to handling problem, smaller males produced no silk for very large flies. Females took more time to digest a nuptial gift with a larger amount of silk than a nuptial gift with a smaller amount of silk. A possible interpretation of the adaptive significance of wrapping is that males use silk to prolong the copulation time during mating

Mean-square stability analysis of approximations of stochastic differential equations in infinite dimensions

The (asymptotic) behaviour of the second moment of solutions to stochastic differential equations is treated in mean-square stability analysis. This property is discussed for approximations of infinite-dimensional stochastic differential equations and necessary and sufficient conditions ensuring mean-square stability are given. They are applied to typical discretization schemes such as combinations of spectral Galerkin, finite element, Euler-Maruyama, Milstein, Crank-Nicolson, and forward and backward Euler methods. Furthermore, results on the relation to stability properties of corresponding analytical solutions are provided. Simulations of the stochastic heat equation illustrate the theory.Comment: 22 pages, 4 figures; deleted a section; shortened the presentation of results; corrected typo

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

Housing Cooperatives and Social Capital: The Case of Vienna

Drawing on the case of Vienna, the article examines the role of third sector housing for social cohesion in the city. With the joint examination of an organisational and an institutional level of housing governance, the authors apply an interdisciplinary, multi-level research approach which aims at contributing to a comprehensive understanding of social cohesion as a contextualised phenomenon which requires place-based as well as structural (multi-level) solutions. Using a large-scale household survey and interviews with key informants, the analysis shows an ambiguous role housing cooperatives play for social cohesion: With the practice of “theme-oriented housing estates”, non-profit housing returns to the traditional cooperative principle of Gemeinschaft. However, community cooperatives rather promote homogenous membership and thus, encompass the danger to establish cohesive islands that are cut off from the rest of the city. Furthermore, given the solidarity-based housing regime of Vienna, fostering bonding social capital on the neighbourhood level, might anyway just be an additional safeguarding mechanism for social cohesion. More important is the direct link between the micro-level of residents and the macro-level of urban housing policy. In this respect, cooperative housing represents a crucial intermediate level that strengthens the linking social capital of residents and provides opportunity structures for citizen participation. However, the increasing adoption of a corporate management orientation leads to a hollowing out of the cooperative principle of democratic member participation, reducing it to an informal and non-binding substitute. Thus, it is in the responsibility of both managements and residents to revitalise the existing democratic governance structures of cooperative housing before they will be completely dismantled by market liberalization and privatization. In contrast to other European cities, third sector housing in Vienna has the potential to give residents a voice beyond the neighbourhood and the field of housing.Social Housing, Third Sector Housing, Housing Cooperatives, Social Cohesion, Social Capital, Governance

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: Analyzing Bayesian Structural 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 realistic modeling of complex problems. This paper describes the capabilities of the free software package BayesX for estimating regression models with structured additive predictor based on MCMC inference. The program extends the capabilities of existing software for semiparametric regression included in S-PLUS, SAS, R or Stata. 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, negative Binomial, zero inflated Poisson and zero inflated 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 can be estimated. Moreover, BayesX allows the estimation of complex continuous time survival and hazard rate models.