On the J-test for nonnested hypotheses and Bayesian extension

Abstract Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test will either reject, or fail to reject both specifications. In such cases we show that it is possible to use the information generated in the process of applying the J-test to implement a Bayesian approach that provides an unequivocal and acceptable solution. Jeffreys’ Bayes factors offer ways of obtaining the posterior probabilities of the competing models and relative ranking of the competing hypotheses. We further show that by using approximations of Schwarz Information Criterion and Bayesian Information Criterion we can use the classical estimates of the log of the maximum likelihood which are available from the estimation procedures used to implement the J test to obtain Bayesian posterior odds and posterior probabilities of the competing nested and non- nested specifications without having to specify prior distributions and going through the rigorous Bayesian computations.specification testing, non-nested hypotheses, Bayes factor, Bayesian Information Criteria, Marginal likelihood

Improved approximation bounds for Vector Bin Packing

In this paper we propose an improved approximation scheme for the Vector Bin Packing problem (VBP), based on the combination of (near-)optimal solution of the Linear Programming (LP) relaxation and a greedy (modified first-fit) heuristic. The Vector Bin Packing problem of higher dimension (d \geq 2) is not known to have asymptotic polynomial-time approximation schemes (unless P = NP). Our algorithm improves over the previously-known guarantee of (ln d + 1 + epsilon) by Bansal et al. [1] for higher dimensions (d > 2). We provide a {\theta}(1) approximation scheme for certain set of inputs for any dimension d. More precisely, we provide a 2-OPT algorithm, a result which is irrespective of the number of dimensions d.Comment: 15 pages, 3 algorithm

Coal in Alaska requirements to enhance environmentally sound use in both domestic and Pacific Rim markets

This document originates from three meetings held in 1989 with the leaders of the Alaskan Coal Industry and coal technologists from the U.S. Department of Energy (DOE)~ Mineral Industry Research Laboratory (MIRL) and Geophysical Institute - University of Alaska Fairbanks, the Alaska Department of Natural Resources, the Alaska Science and Technology Commission, several of the Alaska Native Corporations, and a number of coal experts from private industries. The information included is intended to illustrate the vast resource base and quality of Alaskan coals, show the projected size of the Pacific Rim steam coal market, discuss policy changes necessary to facilitate the development of an expanded coal industry, and describe the technology development needs for Alaskan coals to compete in the world market. It is aimed at increasing the general knowledge about the potential of coal in Alaska and providing data for use in marketing the resource.Prepared for the Governor and Legislators - State of Alaska under the Direction of Dr. Henry Cole, Science and Technology Advisor. Technical Editor - Dr. Warrack G. Willson, Energy and Environmental Research Center, University of North Dakota; and Mineral Industry Research Laboratory, University of Alaska Fairbanks. Co-authors - W. (Bill) Irwin, Consultant, Calgary, Alberta; Dr. John Sims, Usibelli Coal Mine Inc.; Dr. p.o. Rao, Mineral Industry Research Laboratory; and Bill Noll, Suneel Alaska Corp

Sampling Random Spanning Trees Faster than Matrix Multiplication

We present an algorithm that, with high probability, generates a random spanning tree from an edge-weighted undirected graph in $\tilde{O}(n^{4/3}m^{1/2}+n^{2})$ time (The $\tilde{O}(\cdot)$ notation hides $\operatorname{polylog}(n)$ factors). The tree is sampled from a distribution where the probability of each tree is proportional to the product of its edge weights. This improves upon the previous best algorithm due to Colbourn et al. that runs in matrix multiplication time, $O(n^\omega)$. For the special case of unweighted graphs, this improves upon the best previously known running time of $\tilde{O}(\min\{n^{\omega},m\sqrt{n},m^{4/3}\})$ for $m \gg n^{5/3}$ (Colbourn et al. '96, Kelner-Madry '09, Madry et al. '15). The effective resistance metric is essential to our algorithm, as in the work of Madry et al., but we eschew determinant-based and random walk-based techniques used by previous algorithms. Instead, our algorithm is based on Gaussian elimination, and the fact that effective resistance is preserved in the graph resulting from eliminating a subset of vertices (called a Schur complement). As part of our algorithm, we show how to compute $\epsilon$-approximate effective resistances for a set $S$ of vertex pairs via approximate Schur complements in $\tilde{O}(m+(n + |S|)\epsilon^{-2})$ time, without using the Johnson-Lindenstrauss lemma which requires $\tilde{O}( \min\{(m + |S|)\epsilon^{-2}, m+n\epsilon^{-4} +|S|\epsilon^{-2}\})$ time. We combine this approximation procedure with an error correction procedure for handing edges where our estimate isn't sufficiently accurate

Effect of neurostimulation on cognition and mood in refractory epilepsy.

Epilepsy is a common, debilitating neurological disorder characterized by recurrent seizures. Mood disorders and cognitive deficits are common comorbidities in epilepsy that, like seizures, profoundly influence quality of life and can be difficult to treat. For patients with refractory epilepsy who are not candidates for resection, neurostimulation, the electrical modulation of epileptogenic brain tissue, is an emerging treatment alternative. Several forms of neurostimulation are currently available, and therapy selection hinges on relative efficacy for seizure control and amelioration of neuropsychiatric comorbidities. Here, we review the current evidence for how invasive and noninvasive neurostimulation therapies affect mood and cognition in persons with epilepsy. Invasive therapies include vagus nerve stimulation (VNS), deep brain stimulation (DBS), and responsive neurostimulation (RNS). Noninvasive therapies include trigeminal nerve stimulation (TNS), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS). Overall, current evidence supports stable cognition and mood with all neurostimulation therapies, although there is some evidence that cognition and mood may improve with invasive forms of neurostimulation. More research is required to optimize the effects of neurostimulation for improvements in cognition and mood

Land Rental Markets in India: Efficiency and Equity Considerations

Despite the fact that land rental is restricted to varying degrees in India, the participation in this market is widespread and it is observed to operate relatively efficiently in 12 villages studied in Andhra Pradesh and Madhya Pradesh. The estimated probit models predicted that the rental market transferred land to those with relatively smaller holdings, but greater ability to make productive use of land, more assets to invest, more adults available for labour and fewer off-farm opportunities. Also land is rented out predominantly to younger farmers and to farmers not involved in off-farm jobs. Renting in is predicted to be relatively higher in the villages which are remote and weakly integrated into mainstream infrastructure and institutions. Land rental markets make an important contribution towards land use redistribution in the villages where land rental is high and where land distribution without land rental is relatively more unequal. The study recommends that existing policies restricting land rental should be eased, and investment towards infrastructure development and off-farm employment generating projects expanded.land rental markets, economic efficiency, equity, India, Land Economics/Use,

Addressing the Challenges of Semantic Citizen-Sensing

Preprin

Gaia DR2 Distances and Peculiar Velocities for Galactic Black Hole Transients

We report on a first census of Galactic black hole X-ray binary (BHXRB) properties with the second data release (DR2) of {\em Gaia}, focusing on dynamically confirmed and strong candidate black hole transients. DR2 provides five-parameter astrometric solutions including position, parallax and proper motion for 11 of a sample of 24 systems. Distance estimates are tested with parallax inversion as well as Bayesian inference. We derive an empirically motivated characteristic scale length of $L$=2.17$\pm$0.12 kpc for this BHXRB population to infer distances based upon an exponentially decreasing space density prior. Geometric DR2 parallaxes provide new, independent distance estimates, but the faintness of this population in quiescence results in relatively large fractional distance uncertainties. Despite this, DR2 estimates generally agree with literature distances. The most discrepant case is BW Cir, for which detailed studies of the donor star have suggested a distant location at >~25 kpc. A large DR2 measured parallax and relatively high proper motion instead prefer significantly smaller distances, suggesting that the source may instead be amongst the nearest of XRBs. However, both distances create problems for interpretation of the source, and follow-up data are required to resolve its true nature. DR2 also provides a first distance estimate to one source, MAXI J1820+070, and novel proper motion estimates for 7 sources. Peculiar velocities relative to Galactic rotation exceed $\sim$ 50 km s$^{-1}$ for the bulk of the sample, with a median system kinetic energy of peculiar motion of $\sim$ 5 $\times$ 10$^{47}$ erg. BW Cir could be a new high-velocity BHXRB if its astrometry is confirmed. A putative anti-correlation between peculiar velocity and black hole mass is found, as expected in mass-dependent BH kick formation channels, but this trend remains weak in the DR2 data.Comment: MNRAS in pres