Valid Asymptotic Expansions for the Maximum Likelihood Estimator of the Parameter of a Stationary, Gaussian, Strongly Dependent Process

We establish the validity of an Edgeworth expansion to the distribution of the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process. The result covers ARFIMA type models, including fractional Gaussian noise. The method of proof consists of three main ingredients: (i) verification of a suitably modified version of Durbin's (1980) general conditions for the validity of the Edgeworth expansion to the joint density of the log-likelihood derivatives; (ii) appeal to a simple result of Skovgaard (1986) to obtain from this an Edgeworth expansion for the joint distribution of the log-likelihood derivatives; (iii) appeal to and extension of arguments of Bhattacharya and Ghosh (1978) to accomplish the passage from the result on the log-likelihood derivatives to the result for the maximum likelihood estimators. We develop and make extensive use of a uniform version of Dahlhaus's (1989) Theorem 5.1 on products of Toeplitz matrices; the extension of Dahlhaus's result is of interest in its own right. A small numerical study of the efficacy of the Edgeworth expansion is presented for the case of fractional Gaussian noise.Edgeworth expansions, long memory processes, ARFIMA models

From Passive Victims to Partners in Their Own Reintegration: Civil society’s role in empowering returned Thai fishermen

Despite the significant international attention to human trafficking in the fishing industry in Southeast Asia, victims continue to experience poor outcomes after their return to Thailand. The Labour Rights Promotion Network (LPN) has assisted many returned fishermen in the difficult journey that begins after their rescue and repatriation. In this paper, we argue that the poor outcomes are the product of systemic failures in the aftercare processes, which are not sufficiently victim-centred and discourage trafficked fishermen’s participation in prosecutions. This is the case in the criminal justice system, where flaws in victim identification and evidence collection can undermine trafficked persons’ rights and make it extremely difficult for them to obtain compensation—a significant factor in their recovery and reintegration. This same cycle of disenfranchisement is pervasive in reintegration services at large in Thailand, many of which are overly paternalistic and neglect survivors’ individual needs and interests. Civil society organisations can remediate these problems by supporting the government in its efforts to strengthen prosecutions and make the criminal justice system more victim-friendly. More broadly, civil society can contribute to a victim-centred approach that places aftercare in a larger perspective—one that extends beyond the purview of the criminal justice system. This paper will examine two emerging models in post-trafficking service provision: Unconditional Cash Transfers (UCTs) and volunteer social networks, which recognise victim empowerment not just as a means towards better law enforcement, but as an end in itself

Medicaid Spending Growth Over the Last Decade and the Great Recession, 2000-2009

Analyzes Medicaid enrollment and per-capita spending growth by service and compared with other areas of the healthcare system. Examines contributing factors, potential program cuts as a result of states' budget woes, and implications for the safety net

Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning

Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In the last decade, decision diagrams (DDs) have brought a new perspective on obtaining upper and lower bounds that can be significantly better than classical bounding mechanisms, such as linear relaxations. It is well known that the quality of the bounds achieved through this flexible bounding method is highly reliant on the ordering of variables chosen for building the diagram, and finding an ordering that optimizes standard metrics is an NP-hard problem. In this paper, we propose an innovative and generic approach based on deep reinforcement learning for obtaining an ordering for tightening the bounds obtained with relaxed and restricted DDs. We apply the approach to both the Maximum Independent Set Problem and the Maximum Cut Problem. Experimental results on synthetic instances show that the deep reinforcement learning approach, by achieving tighter objective function bounds, generally outperforms ordering methods commonly used in the literature when the distribution of instances is known. To the best knowledge of the authors, this is the first paper to apply machine learning to directly improve relaxation bounds obtained by general-purpose bounding mechanisms for combinatorial optimization problems.Comment: Accepted and presented at AAAI'1

War and Rights: The Impact of War on Political and Civil Rights

How does warfare impact the political and civil liberties of men, women, and minorities? Hintze (1906) and Lasswell (1941) argue states facing a severe security threat are likely to reduce rights in order to minimize domestic opposition to the war and maximize mobilization potential. Downing (1992) and Klinkner and Smith (1999) argue that under certain circumstances mobilization for war can unintentionally lead to an expansion of rights. This presentation explores these arguments with finding from historical case studies (e.g., Imperial Russia, Austro-Hungarian Dual Monarchy, African Americans in World War I and II, and Tirailleurs Senegalese in World War I)

Beyond the 2nd Fermi Pulsar Catalog

Over thirteen times more gamma-ray pulsars have now been studied with the Large Area Telescope on NASA's Fermi satellite than the ten seen with the Compton Gamma-Ray Observatory in the nineteen-nineties. The large sample is diverse, allowing better understanding both of the pulsars themselves and of their roles in various cosmic processes. Here we explore the prospects for even more gamma-ray pulsars as Fermi enters the 2nd half of its nominal ten-year mission. New pulsars will naturally tend to be fainter than the first ones discovered. Some of them will have unusual characteristics compared to the current population, which may help discriminate between models. We illustrate a vision of the future with a sample of six pulsars discovered after the 2nd Fermi Pulsar Catalog was written.Comment: 6 pages, to appear in the proceedings of "The Fast and the Furious: Energetic Phenomena in Isolated Neutron Stars, Pulsar Wind Nebulae and Supernova Remnants",ESAC, Madrid, Spain, 22 - 24 May 2013 http://xmm.esac.esa.int/external/xmm_science/workshops/2013_science/, to be published as a regular issue of the Astronomische Nachrichten / Astronomical Notes (AN

Kinetic self-organization of trenched templates for the fabrication of versatile ferromagnetic nanowires

We have self-organized versatile magnetic nanowires, ie with variable period and adjustable magnetic anisotropy energy (MAE). First, using the kinetic roughening of W(110) uniaxial templates of trenches were grown on commercial Sapphire wafers. Unlike most templates used for self-organization, those have a variable period, 4-12nm are demonstrated here. Fe deposition then results in the formation of wires in the trenches. The magnitude of MAE could be engineered up or down by changing the capping- or underlayer, in turn affecting the mean superparamagnetic temperature, raised to 175K so far.Comment: 3 page

A la rencontre des savants piémontais sur les pas de Jérôme Lalande dans son Voyage d'Italie 1765-1768

Le voyage de Jérôme Lalande offre une vision des savants dans le royaume de Piémont-Sardaigne dans la seconde moitié du XVIIIe siècle

Asymptotic Properties of Approximate Bayesian Computation

Approximate Bayesian computation allows for statistical analysis in models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, its limiting shape, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.Comment: This 31 pages paper is a revised version of the paper, including supplementary materia