Interaction of minor ions with fast and slow shocks

The coronal slow shock was predicted to exist embedded in large coronal holes at 4 to 10 solar radii. A three-fluid model was used to study the jumps in minor ions propertes across the coronal slow shock. The jump conditions were formulated in the de Hoffmann-Teller frame of reference. The Rankine-Hugoniot solution determines the MHD flow and the magnetic field across the shocks. For each minor ion species, the fluid equations for the conservation of mass, momentum, and energy can be solved to determine the velocity and the temperature of the ions across the shock. A simularity solution was also obtained for heavy ions. The results show that on the downstream side of the coronal slow shock the ion temperatures are nearly proportional to the ion masses for He, O, Si, and Fe in agreement with observed ion temperatures in the inner solar wind. This indicates that the possibly existing coronal slow shock can be responsible for the observed heating of minor ions in the solar wind

Conversion of magnetic field energy into kinetic energy in the solar wind

The outflow of the solar magnetic field energy (the radial component of the Poynting vector) per steradian is inversely proportional to the solar wind velocity. It is a decreasing function of the heliocentric distance. When the magnetic field effect is included in the one-fluid model of the solar wind, the transformation of magnetic field energy into kinetic energy during the expansion process increases the solar wind velocity at 1 AU by 17 percent

Radiative transfer for parallel streams of radiating gases

Radiative and convective heat transfer between two parallel streams of absorbing and emitting radiating gase

The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series

This paper derives the asymptotic distribution of a smoothing-based estimator of the Lyapunov exponent for a stochastic time series under two general scenarios. In the first case, we are able to establish root-T consistency and asymptotic normality, while in the second case, which is more relevant for chaotic processes, we are only able to establish asymptotic normality at a slower rate of convergence. We provide consistent confidence intervals for both cases. We apply our procedures to simulated data.Chaos, kernel, nonlinear dynamics, nonparametric regression, semiparametric

Nonparametric Estimation with Aggregated Data

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic normality for our procedures. As usual, the rates of convergence can be very slow depending on the behaviour of the characteristic function at infinity. We investigate the practical performance of our method in a simple Monte Carlo experimentAggregated data, deconvolution, grouped data, kernel, nonparametric regression

A Quantilogram Approach to Evaluating Directional Predictability

In this note we propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on the correlogram of quantile hits. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to stock index return data. The empirical results suggest some directional predictability in returns, especially in mid-range quantiles like 5%-10%.Correlogram, dependence, efficient markets, quantiles.