Malliavin calculus for backward stochastic differential equations and application to numerical solutions

In this paper we study backward stochastic differential equations with general terminal value and general random generator. In particular, we do not require the terminal value be given by a forward diffusion equation. The randomness of the generator does not need to be from a forward equation, either. Motivated from applications to numerical simulations, first we obtain the $L^p$-H\"{o}lder continuity of the solution. Then we construct several numerical approximation schemes for backward stochastic differential equations and obtain the rate of convergence of the schemes based on the obtained $L^p$-H\"{o}lder continuity results. The main tool is the Malliavin calculus.Comment: Published in at http://dx.doi.org/10.1214/11-AAP762 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Temporal Topic Analysis with Endogenous and Exogenous Processes

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com

Optimal Management of a Eutrophied Coastal Ecosystem: Balancing Agricultural and Municipal Abatement Measures

Agriculture and municipal wastewater are the principal sources of eutrophying nutrients in many water ecosystems. We develop a model which considers the characteristics of agricultural and municipal nutrient abatement. The model explicitly accounts for the investment needed to set up wastewater treatment facilities, and makes it possible to determine the optimal timing of investment as well as the optimal agricultural and municipal abatement levels. We apply the model to the Finnish coastal waters of the Gulf of Finland. Our results indicate that substantial savings in abatement costs and the damage associated with eutrophication could be obtained by constructing the facilities needed to process all the wastewaters entering the coastal ecosystem. The optimal timing of investment is shown to hinge on both the economic and ecological characteristics of the ecosystem.Resource /Energy Economics and Policy,

Clues for the existence of two K1(1270)K_1(1270) resonances

The axial vector meson $K_1(1270)$ was studied within the chiral unitary approach, where it was shown that it has a two-pole structure. We reanalyze the high-statistics WA3 experiment $K^- p\to K^-\pi^+\pi^- p$ at 63 GeV, which established the existence of both $K_1(1270)$ and $K_1(1400)$, and we show that it clearly favors our two-pole interpretation. We also reanalyze the traditional K-matrix interpretation of the WA3 data and find that the good fit of the data obtained there comes from large cancellations of terms of unclear physical interpretation.Comment: published version in PRD; typos corrected; title changed to "Clues for the existence of two $K_1(1270)$ resonances

Modeling migraine severity with autoregressive ordered probit models

This paper considers the problem of modeling migraine severity assessments and their dependence on weather and time characteristics. Since ordinal severity measurements arise from a single patient dependencies among the measurements have to be accounted for. For this the autore- gressive ordinal probit (AOP) model of Müller and Czado (2004) is utilized and fitted by a grouped move multigrid Monte Carlo (GM-MGMC) Gibbs sampler. Initially, covariates are selected using proportional odds models ignoring this dependency. Model fit and model comparison are discussed. The analysis shows that humidity, windchill, sunshine length and pressure differences have an effect in addition to a high dependence on previous measurements. A comparison with proportional odds specifications shows that the AOP models are preferred