On Nonregularized Estimation of Psychological Networks.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through off-diagonal elements in the precision matrix. While the graphical Lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this article, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted ( p≪n ). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce nonregularized methods based on multiple regression and a nonparametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the nonregularized methods can be used to reduce the false-positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The nonregularized methods have been implemented in the R package GGMnonreg

OFFERING FOOD DISTRIBUTION-RELATED COURSES IN AGRICULTURAL ECONOMICS CURRICULA: PERSPECTIVES FROM ACROSS THE U.S.

Teaching/Communication/Extension/Profession,

Industry-Academic Partnerships – Benefit or Burden?

In an applied discipline such as agribusiness management, there are many opportunities for collaboration between academia and industry. This article highlights opportunities for industry-academic partnerships through research, sabbatical leaves, consulting, outreach, student enrichment activities, and industry advisory boards. The principal benefits and pitfalls associated with each type of collaboration are discussed along with tips for managing industry-academic partnerships.industry partnerships, industry collaboration, Industrial Organization, Teaching/Communication/Extension/Profession, Q10,

How Agricultural Economists Increase the Value of Agribusiness Research

Historically, there has been declining cooperation between agribusiness firms and agricultural economists. In new product marketing research, firms' tend to conduct their own analyses, partially due to confidentiality, usually consisting of simple univariate or bivariate statistics such as chi-squared tests of independence. The primary objective of this paper is to demonstrate, through a case study, one way in which agricultural economists can add value to agribusiness firms research. Results from the econometric model offer a richer explanation of consumer behavior and may be more useful to agribusiness firms.Teaching/Communication/Extension/Profession,

Electric Current Tuning the Self-Oscillation Frequency of EC-VCSELs

published_or_final_versio

Simulation of Capture Behaviour in IEEE 802.11 Radio Modems

In this paper we investigate the performance of common capture models in terms of the fairness properties they reflect across contenting hidden connections. We propose a new capture model, Message Retraining,as a means of providing an accurate description of experimental data. Using two fairness indices we undertake a quantitative study of the accuracy with which each capture model is able to reflect experimental data. Standard capture models are shown to be unable to accurately reflect the fairness properties of empirical data. The Message Retraining capture model is shown to provide a good estimate of actual system performance in varying signal strength conditions

Stick boundary conditions and rotational velocity auto-correlation functions for colloidal particles in a coarse-grained representation of the solvent

We show how to implement stick boundary conditions for a spherical colloid in a solvent that is coarse-grained by the method of stochastic rotation dynamics. This allows us to measure colloidal rotational velocity auto-correlation functions by direct computer simulation. We find quantitative agreement with Enskog theory for short times and with hydrodynamic mode-coupling theory for longer times. For aqueous colloidal suspensions, the Enskog contribution to the rotational friction is larger than the hydrodynamic one when the colloidal radius drops below 35nm.Comment: new version with some minor change

J/psi production at RHIC-PHENIX

The J/psi is considered to be among the most important probes for the deconfined quark gluon plasma (QGP) created by relativistic heavy ion collisions. While the J/psi is thought to dissociate in the QGP by Debye color screening, there are competing effects from cold nuclear matter (CNM), feed-downs from excited charmonia (chi_c and psi') and bottom quarks, and regeneration from uncorrelated charm quarks. Measurements that can provide information to disentangle these effects are presented in this paper.Comment: 4 pages, 3 figures, conference proceedings: the 20th International Conference on Ultra-Relativistic Nucleus-Nucleus Collisions, Quark Matter 2008, Jaipur (India), 4-10 February 2008, submitted to J. Phys. G: Nuclear and Particle Physic