Modeling Transport Mode Decisions Using Hierarchical Binary Spatial Regression Models with Cluster Effects

This work is motivated by a mobility study conducted in the city of Munich, Germany. The variable of interest is a binary response, which indicates whether public transport has been utilized or not. One of the central questions is to identify areas of low/high utilization of public transport after adjusting for explanatory factors such as trip, individual and household attributes. The goal is to develop flexible statistical models for a binary response with covariate, spatial and cluster effects. One approach for modeling spatial effects are Markov Random Fields (MRF). A modification of a class of MRF models with proper joint distributions introduced by Pettitt et al. (2002) is developed. This modification has the desirable property to contain the intrinsic MRF in the limit and still allows for efficient spatial parameter updates in Markov Chain Monte Carlo (MCMC) algorithms. In addition to spatial effects, cluster effects are taken into consideration. Group and individual approaches for modeling these effects are suggested. The first one models heterogeneity between clusters, while the second one models heterogeneity within clusters. A naive approach to include individual cluster effects results in an unidentifiable model. It is shown how an appropriate reparametrization gives identifiable parameters. This provides a new approach for modeling heterogeneity within clusters. For hierarchical spatial binary regression models with individual cluster effects two MCMC algorithms for parameter estimation are developed. The first one is based on a direct evaluation of the likelihood. The second one is based on the representation of binary responses with Gaussian latent variables through a threshold mechanism, which is particularly useful for probit models. Simulation results show a satisfactory behavior of the MCMC algorithms developed. Finally the proposed model classes are applied to the mobility study and results are interpreted

A survey of functional laws of the iterated logarithm for self-similar processes

A process X(t) is self-similar with index H > 0 if the finite-dimensional distributions of X(at) are identical to those of aHX(t) for all a > 0. Consider self-similar processes X(t) that are Gaussian or that can be represented throught Wiener-Itô integrals. The paper surveys functional laws of the iterated logarithm for such processes X(t) and for sequences whose normalized sums coverage weakly to X(t). The goal is to motivate the results by including outline of proofs and by highlighting relationships between the various assumptions. The paper starts with a general discussion fo functional laws of the iterated logarithm, states some of their formulations and sketches the reproducing kernal Hilbert space set-up.ECS-80-15585 - National Science Foundatio

Consequences of Content Diversity for Online Public Spaces for Local Communities

While there is significant potential for social technologies to strengthen local communities, creating viable online spaces for them remains difficult. Maintaining a reliable content stream is challenging for local communities with their bounded emphases and limited population of potential contributors. Some systems focus on specific information types (e.g. restaurant, events). Others allow many different information types. This paper reports our findings about the consequences of content diversity from a study of neighborhood-oriented Facebook groups. The findings raise questions about the viability of designs for local online communities that focus narrowly on single topics, goals, and audiences

Energy consumption, income and CO2 emissions in Latin America

I describe and compare the environment policies of European Union and of 12 Latin Americans economies. For this, I use common statistical methods, such as non-parametric tests, convergence analysis (Beta and Sigma) and panel data, in order to verify the hypothesis that emissions and energy use in Latin America has been increasing since the mid-20th century. The statistical tests used confirm the proposed hypothesis. I also rely upon the Environmental Kuznets Curve- whereby economies that are at the growth stage are more focused on achieving the latter than they are on environmental concerns and those which have already achieved growth focus more on environmental concerns-to take an alternative approach by introducing the role of economic growth in the evolution of energy consumption and emissions. This chapter reaches the conclusion that energy consumption and pollutant emissions in LA, in per capita terms, are converging. This suggests that the initial levels of the variables help to explain why some countries have increased emissions (in this case, energy consumption) to a greater extent than other economies in the region. Evidence of convergence is also found, as well as a monotonic relationship between the level of pollution and the level of development (consistent with the Environmental Kuznets Curve).Energy Consumption, emissions, Latin America, Convergence

A simple and efficient approach to the optimization of correlated wave functions

We present a simple and efficient method to optimize within energy minimization the determinantal component of the many-body wave functions commonly used in quantum Monte Carlo calculations. The approach obtains the optimal wave function as an approximate perturbative solution of an effective Hamiltonian iteratively constructed via Monte Carlo sampling. The effectiveness of the method as well as its ability to substantially improve the accuracy of quantum Monte Carlo calculations is demonstrated by optimizing a large number of parameters for the ground state of acetone and the difficult case of the $1{}^1{B}_{1u}$ state of hexatriene.Comment: 5 pages, 1 figur