Accelerating linear causal model discovery using hoeffding bounds

Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithms proposed by Dai et al. has demonstrated the ability of the Minimum Message Length (MML) principle in discovering Linear Causal Models from training data. In order to further explore ways to improve efficiency, this paper incorporates the Hoeffding Bounds into the learning process. At each step of causal discovery, if a small number of data items is enough to distinguish the better model from the rest, the computation cost will be reduced by ignoring the other data items. Experiments with data set from related benchmark models indicate that the new algorithm achieves speedup over previous work in terms of learning efficiency while preserving the discovery accuracy.

Lower bounds for stability margin of n-dimensional discrete systems

This paper derives lower bounds for the stability margin of n-dimensional discrete systems in the Roesser’s state space setting. The lower bounds for stability margin are derived based on the MacLaurine series expansion. Numerical examples are given to illustrate the results.

Revisiting the US money demand function: an application of the Lagrange multiplier structural break unit root test and the bounds test for a long-run relationship

In this article, we examine the issue of a levels relationship and stability of the US money demand function over the period 1959:01 to 2004:02. We use the Lagrange multiplier structural break unit root test and the bounds testing approach to a long-run relationship in levels of the variables, namely real money demand, nominal interest rate and real income. We find greater evidence for a long-run relationship in levels and stability of the US money demand function when we use M2 as a proxy for money demand. However, we find little evidence for a long-run relationship between M1 and M2 with their determinants for the recent period, spanning the last decade or so.

Dead man walking : an empirical reassessment of the deterrent effect of capital punishment using the bounds testing approach to cointegration

This paper empirically estimates a murder supply equation for the United States from 1965 to 2001 within a cointegration and error correction framework. Our findings suggest that any support for the deterrence hypothesis is sensitive to the inclusion of variables for the effects of guns and other crimes. In the long run we find that real income and the conditional probability of receiving the death sentence are the main factors explaining variations in the homicide rate. In the short run the aggravated assault rate and robbery rate are the most important determinants of the homicide rate.

Japan's fertility transition : empirical evidence from the bounds testing approach to cointegration

This study investigates the determinants of the fertility rate in Japan over the 1950–2000 period. We use, for the first time in the fertility literature, the bounds testing approach to cointegration. Amongst our key results, we find that, in both the short-run and long-run, the use of contraceptives and abortion have significantly contributed to the fertility decline in Japan. We also find statistically significant evidence that increasing age at marriage in Japan and increasing education level of women have contributed negatively to the fertility transition.

The determinants of immigration from Fiji to New Zealand : AN empirical reassessment using the bounds testing approach

This article re-examines Gani's (1998) findings on the determinants of migrant flows from Fiji to New Zealand by employing the bounds testing procedure to cointegration, within an autoregressive distributive lag framework. The main findings are that in the long run all variables are statistically insignificant, although correctly signed with the exception of the unemployment differential. In the short run, in sharp contrast to Gani's (1998) findings, political instability is consistently the most important determinant of migration flows while the standard of living and real wage differentials are statistically insignificant across all specifications.

Modelling immigration flows : an application of the bounds test to Fiji - Australia migration

This paper examines the long-run and short-run determinants of migration from Fiji to Australia between 1972 and 2001 using a human capital framework, which is extended to take account of political instability in Fiji. Our main findings are that in the long run the real wage differential and political instability in Fiji are the main determinants. In the short run, there is some evidence that the wage differential and transport costs are important factors, but this finding is not robust across all specifications. Lagged migration and political instability are the most important determinants in the short run.

The residential demand for electricity in Australia : an application of the bounds testing approach to cointegration

This paper reports estimates of the long- and short-run elasticities of residential demand for electricity in Australia using the bounds testing procedure to cointegration, within an autoregressive distributive lag framework. In the long run, we find that income and own price are the most important determinants of residential electricity demand, while temperature is significant some of the time and gas prices are insignificant. Our estimates of long-run income elasticity and price elasticity of demand are consistent with previous studies, although they are towards the lower end of existing estimates. As expected, the short-run elasticities are much smaller than the long-run elasticities, and the coefficients on the error-correction coefficients are small consistent with the fact that in the short-run energy appliances are fixed.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

Evidence from genetic and Lagrangian drifter data for transatlantic transport of small juvenile green turtles

Aim  A key life-history component for many animals is the need for movement between different geographical locations at particular times. Green turtle (Chelonia mydas) hatchlings disperse from their natal location to spend an early pelagic stage in the ocean, followed by a neritic stage where small juveniles settle in coastal areas. In this study, we combined genetic and Lagrangian drifter data to investigate the connectivity between natal and foraging locations. In particular we focus on the evidence for transatlantic transport. Location  Atlantic Ocean.

Methods  We used mitochondrial DNA (mtDNA) sequences (n = 1567) from foraging groups (n = 8) and nesting populations (n = 12) on both sides of the Atlantic. Genetic data were obtained for Cape Verde juvenile turtles, a foraging group not previously sampled for genetic study. Various statistical methods were used to explore spatial genetics and population genetic structure (e.g. exact tests of differentiation, Geneland and analysis of molecular variance). Many-to-many mixed stock analysis estimated the connectivity between nesting and foraging groups.

Results  Our key new finding is robust evidence for connectivity between a nesting population on the South American coast (25% of the Surinam nesting population are estimated to go to Cape Verde) and a foraging group off the coast of West Africa (38% of Cape Verde juveniles are estimated to originate from Surinam), thus extending the results of previous investigations by confirming that there is substantial transatlantic dispersal in both directions. Lagrangian drifter data demonstrated that transport by drift across the Atlantic within a few years is possible.

Main conclusions  Small juvenile green turtles seem capable of dispersing extensively, and can drop out of the pelagic phase on a transatlantic scale (the average distance between natal and foraging locations was 3048 km). Nevertheless, we also find support for the ‘closest-to-home’ hypothesis in that the degree of contribution from a nesting population to a foraging group is correlated with proximity. Larger-sized turtles appear to feed closer to their natal breeding grounds (the average distance was 1133 km), indicating that those that have been initially transported to far-flung foraging grounds may still be able to move nearer to home as they grow larger.

Ontogenetic development of migration : Lagrangian drift trajectories suggest a new paradigm for sea turtles

Long distance migration occurs in a wide variety of taxa including birds, insects, fishes, mammals and reptiles. Here, we provide evidence for a new paradigm for the determinants of migration destination. As adults, sea turtles show fidelity to their natal nesting areas and then at the end of the breeding season may migrate to distant foraging sites. For a major rookery in the Mediterranean, we simulated hatchling drift by releasing 288 000 numerical particles in an area close to the nesting beaches. We show that the pattern of adult dispersion from the breeding area reflects the extent of passive dispersion that would be experienced by hatchlings. Hence, the prevailing oceanography around nesting areas may be crucial to the selection of foraging sites used by adult sea turtles. This environmental forcing may allow the rapid evolution of new migration destinations if ocean currents alter with climate change.