Numerical Computing and Graphics for the Power Method Transformation Using Mathematica

This paper provides the requisite information and description of software that perform numerical computations and graphics for the power method polynomial transformation. The software developed is written in the Mathematica 5.2 package PowerMethod.m and is associated with fifth-order polynomials that are used for simulating univariate and multivariate non-normal distributions. The package is flexible enough to allow a user the choice to model theoretical pdfs, empirical data, or a user's own selected distribution(s). The primary functions perform the following (a) compute standardized cumulants and polynomial coefficients, (b) ensure that polynomial transformations yield valid pdfs, and (c) graph power method pdfs and cdfs. Other functions compute cumulative probabilities, modes, trimmed means, intermediate correlations, or perform the graphics associated with fitting power method pdfs to either empirical or theoretical distributions. Numerical examples and Monte Carlo results are provided to demonstrate and validate the use of the software package. The notebook Demo.nb is also provided as a guide for user of the power method.

JMASM24: Numerical Computing for Third-Order Power Method Polynomials (Excel)

The power method polynomial transformation is a popular procedure used for simulating univariate and multivariate non-normal distributions. It requires software that solves simultaneous nonlinear equations. Potential users of the power method may not have access to commercial software packages (e.g., Mathematica, Fortran). Therefore, algorithms are presented in the more commonly available Excel 2003 spreadsheets. The algorithms solve for (1) coefficients for polynomials of order three, (2) intermediate correlations and Cholesky factorizations for multivariate data generation, and (3) the values of skew and kurtosis for determining if a transformation will produce a valid power method probability density function (pdf). The Excel files are available at http://www.siu.edu/~epse1/headrick/PowerMethod3rd/ or can be requested from the author at [email protected]

ARE CROP YIELDS NORMALLY DISTRIBUTED?

This paper revisits the issue of crop yield distributions using improved model specifications, estimation and testing procedures that address the methodological concerns raised in recent literature that could have invalidated previous conclusions of yield non-normality. It shows beyond reasonable doubt that some crop yield distributions are non-normal, kurtotic and right or left skewed, depending on the circumstances. A procedure to jointly estimate non-normal farm- and aggregate-level yield distributions with similar means but different variances is illustrated, and the consequences of incorrectly assuming yield normality are explored.Yield non-normality, probability distribution function models, Corn Belt yields, West Texas dryland cotton yields, Crop Production/Industries,

Study on SPH Viscosity Term Formulations

For viscosity-dominated flows, the viscous effect plays a much more important role. Since the viscosity term in SPH-governing (Smoothed Particle Hydrodynamics) equations involves the discretization of a second-order derivative, its treatment could be much more challenging than that of a first-order derivative, such as the pressure gradient. The present paper summarizes a series of improved methods for modeling the second-order viscosity force term. By using a benchmark patch test, the numerical accuracy and efficiency of different approaches are evaluated under both uniform and non-uniform particle configurations. Then these viscosity force models are used to compute a documented lid-driven cavity flow and its interaction with a cylinder, from which the most recommended viscosity term formulation has been identified

Numerical study of halo concentrations in dark-energy cosmologies

We study the concentration parameters, their mass dependence and redshift evolution, of dark-matter halos in different dark-energy cosmologies with constant and time-variable equation of state, and compare them with "standard'' Lambda-CDM and OCDM models. We find that previously proposed algorithms for predicting halo concentrations can be well adapted to dark-energy models. When centred on the analytically expected values, halo concentrations show a log-normal distribution with a uniform standard deviation of ~0.2. The dependence of averaged halo concentrations on mass and redshift permits a simple fit of the form (1+z) c=c0 (M/M0)^a, with a~-0.1 throughout. We find that the cluster concentration depends on the dark energy equation of state at the cluster formation redshift z_{coll} through the linear growth factor D_+(z_{coll}). As a simple correction accounting for dark-energy cosmologies, we propose scaling c0 from Lambda-CDM with the ratio of linear growth factors, c0 -> c0 D_+(z_{coll})/D_{+,Lambda-CDM}(z_{coll}).Comment: 11 pages, submitted to Astronomy & Astrophysic