Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs

Directed acyclic graphs (DAGs) are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of physical, as well as biological systems, where directed edges between nodes represent the influence of components of the system on each other. The general problem of estimating DAGs from observed data is computationally NP-hard, Moreover two directed graphs may be observationally equivalent. When the nodes exhibit a natural ordering, the problem of estimating directed graphs reduces to the problem of estimating the structure of the network. In this paper, we propose a penalized likelihood approach that directly estimates the adjacency matrix of DAGs. Both lasso and adaptive lasso penalties are considered and an efficient algorithm is proposed for estimation of high dimensional DAGs. We study variable selection consistency of the two penalties when the number of variables grows to infinity with the sample size. We show that although lasso can only consistently estimate the true network under stringent assumptions, adaptive lasso achieves this task under mild regularity conditions. The performance of the proposed methods is compared to alternative methods in simulated, as well as real, data examples.Comment: 19 pages, 8 figure

Global Modeling and Prediction of Computer Network Traffic

We develop a probabilistic framework for global modeling of the traffic over a computer network. This model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time--scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain 'space-time' random fields. The model is validated against simulated and real data. It is then applied to predict traffic fluctuations over unobserved links from a limited set of observed links. Further, applications to anomaly detection and network management are briefly discussed

On the formation of Dodd-Frank Act derivatives regulations

Following the 2007-2009 financial crisis, governments around the world passed laws that marked the beginning of new period of enhanced regulation of the financial industry. These laws called for a myriad of new regulations, which in the U.S. are created through the so-called notice-and-comment process. Through examining the text documents generated through this process, we study the formation of regulations to gain insight into how new regulatory regimes are implemented following major laws like the landmark Dodd-Frank Wall Street Reform and Consumer Protection Act. Due to the variety of constituent preferences and political pressures, we find evidence that the government implements rules strategically to extend the regulatory boundary by first pursuing procedural rules that establish how economic activities will be regulated, followed by specifying who is subject to the procedural requirements. Our findings together with the unique nature of the Dodd-Frank Act translate to a number of stylized facts that should guide development of formal models of the rule-making process.National Science Foundation Grant No. 163315

Farmers’ Attitudes Toward Recycled Water Use in Irrigated Agriculture

This study aims to investigate whether farmers are willing to use recycled water for irrigation purposes. It attempts to analyze the attitudinal, socio-demographics and environmental factors that affect a potential user’s acceptance for wastewater reuse. A primary research designed in order to elicit farmers’ preferences and a statistical analysis applied to analyze the relationships among the variables influence their attitudes. The results were obtained from data collected through 302 questionnaires that were answered by the farmers in Nestos catchment, Greece. The research findings might usefully assist policy-makers and planners in the implementation of strategy in water management sector. Farmers’ awareness about the recycling water and their level of acceptance to use it might constitute incoming parameters, on which the decisions in agriculture water planning could be based. Moreover, the identification of factors influencing stakeholders’ acceptance provide the underpinnings for success in any recycling project.     Keywords: public perceptions, behavior analysis, water recycling, integrated water resources management, agriculture water managemen

Hazardous Agrochemicals, Smoking, and Farmers’ Differences in Wage-Risk Tradeoffs

This paper utilizes the theory of compensating differentials for job risks from the labor economics literature to evaluate farmers’ differences in wage-risk tradeoffs. In the context of job risks, the theory predicts that farmers who place a lower value on health status are willing to work for lower compensation on a risky job. The aim of the paper is to evaluate how the observed wage-risk tradeoff is affected by individual heterogeneity in risk preferences, by acknowledging variations in farmers’ revealed attitudes toward risk, both in job-related and non-job activities. The job risk measure employed is self-reported job risk of low back pain, the most recurring health risk faced by farmers. The job-related risky activity is the application of hazardous agrochemicals. The non-job activity is smoking. The primary finding of the study is that individual heterogeneity in risk attitudes is an important determinant of the risk premium workers receive, i.e., individual differences in other health-related activities are influential determinants of the observed wage-risk tradeoff. Keywords:agrochemicals, smoking, farming job risk, compensating differentials, risk preferences, health impairment, Agribusiness, Farm Management, Health Economics and Policy, Labor and Human Capital,

Corrections to Diffusion in Interacting Quantum Systems

The approach to equilibrium in interacting classical and quantum systems is a challenging problem of both theoretical and experimental interest. One useful organizing principle characterizing equilibration is the dissipative universality class, the most prevalent one being diffusion. In this paper, we use the effective field theory (EFT) of diffusion to systematically obtain universal power-law corrections to diffusion. We then employ large-scale simulations of classical and quantum systems to explore their validity. In particular, we find universal scaling functions for the corrections to the dynamical structure factor ⟨⁡(,)⁢⟩, in the presence of a single U⁡(1) or SU⁡(2) charge in systems with and without particle-hole symmetry, and present the framework to generalize the calculation to multiple charges. Classical simulations show remarkable agreement with EFT predictions for subleading corrections, pushing precision tests of effective theories for thermalizing systems to an unprecedented level. Moving to quantum systems, we perform large-scale tensor-network simulations in unitary and noisy 1D Floquet systems with conserved magnetization. We find a qualitative agreement with EFT, which becomes quantitative in the case of noisy systems. Additionally, we show how the knowledge of EFT corrections allows for fitting methods, which can improve the estimation of transport parameters at the intermediate times accessible by simulations and experiments. Finally, we explore nonlinear response in quantum systems and find that EFT provides an accurate prediction for its behavior. Our results provide a basis for a better understanding of the nonlinear phenomena present in thermalizing systems