Lyapunov Exponent and the Solid-Fluid Phase Transition

We study changes in the chaotic properties of a many-body system undergoing a solid-fluid phase transition. To do this, we compute the temperature dependence of the largest Lyapunov exponents $\lambda_{max}$ for both two- and three-dimensional periodic systems of $N$-particles for various densities. The particles interact through a soft-core potential. The two-dimensional system exhibits an apparent second-order phase transition as indicated by a $\lambda$-shaped peak in the specific heat. The first derivative of $\lambda_{max}$ with respect to the temperature shows a peak at the same temperature. The three-dimensional system shows jumps, in both system energy and $\lambda_{max}$, at the same temperature, suggesting a first-order phase transition. Relaxation phenomena in the phase-transition region are analyzed by using the local time averages.Comment: 16 pages, REVTeX, 10 eps figures, epsfig.st

Obesity, disability, and the labor force

Men of prime working age have increased their non-employment rates over the past 30 years, and disability rates have also increased. Many have noted that this increase has happened against a backdrop of generally improving health in the U.S. population. However, obesity has increased substantially over this period. The authors find that changes in the characteristics of male workers—including age, race, ethnicity, and obesity levels—can explain a large portion (around 40 percent) of the increase in non-employment.Obesity ; Unemployment

Backfitting and smooth backfitting for additive quantile models

In this paper, we study the ordinary backfitting and smooth backfitting as methods of fitting additive quantile models. We show that these backfitting quantile estimators are asymptotically equivalent to the corresponding backfitting estimators of the additive components in a specially-designed additive mean regression model. This implies that the theoretical properties of the backfitting quantile estimators are not unlike those of backfitting mean regression estimators. We also assess the finite sample properties of the two backfitting quantile estimators.Comment: Published in at http://dx.doi.org/10.1214/10-AOS808 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org). With Correction