INTEGRATION, RISK, AND SUPPLY RESPONSE: A SIMULATION AND LINEAR PROGRAMMING ANALYSIS OF AN EAST TEXAS COW-CALF PRODUCER

Livestock Production/Industries,

Contrasting patterns of climatic niche divergence in Trebouxia—A alade of lichen-forming algae.

Lichen associations are overwhelmingly supported by carbon produced by photosynthetic algal symbionts. These algae have diversified to occupy nearly all climates and continents; however, we have a limited understanding of how their climatic niches have evolved through time. Here we extend previous work and ask whether phylogenetic signal in, and the evolution of, climatic niche, varies across climatic variables, phylogenetic scales, and among algal lineages in Trebouxia—the most common genus of lichen-forming algae. Our analyses reveal heterogeneous levels of phylogenetic signal across variables, and that contrasting models of evolution underlie the evolution of climatic niche divergence. Together these analyses demonstrate the variable processes responsible for shaping climatic tolerance in Trebouxia, and provide a framework within which to better understand potential responses to climate change associated perturbations. Such predictions reveal a disturbing trend in which the pace at which modern climate change is proceeding will vastly exceed the rate at which Trebouxia climatic niches have previously evolved

A D-vine copula mixed model for joint meta-analysis and comparison of diagnostic tests

For a particular disease, there may be two diagnostic tests developed, where each of the tests is subject to several studies. A quadrivariate generalised linear mixed model (GLMM) has been recently proposed to joint meta-analyse and compare two diagnostic tests. We propose a D-vine copula mixed model for joint meta-analysis and comparison of two diagnostic tests. Our general model includes the quadrivariate GLMM as a special case and can also operate on the original scale of sensitivities and specificities. The method allows the direct calculation of sensitivity and specificity for each test, as well as the parameters of the summary receiver operator characteristic (SROC) curve, along with a comparison between the SROCs of each test. Our methodology is demonstrated with an extensive simulation study and illustrated by meta-analysing two examples where two tests for the diagnosis of a particular disease are compared. Our study suggests that there can be an improvement on GLMM in fit to data since our model can also provide tail dependencies and asymmetries

The role of the stress trap in polariton quasiequilibrium condensation in GaAs microcavities

Recent experiments have shown several effects indicative of Bose-Einstein condensation in polaritons in GaAs-based microcavity structures when a harmonic potential trap for the two-dimensional polaritons is created by applied stress. These effects include both real-space and momentum-space narrowing, first-order coherence, and onset of linear polarization above a particle density threshold. Similar effects have been seen in systems without traps, raising the question of how important the role of the trap is in these experiments. In this paper we present results for both trapped conditions and resonant, non-trapped conditions in the same sample. We find that the results are qualitatively different, with two distinct types of transitions. At low density in the trap, the polaritons remain in the strong-coupling regime while going through the threshold for onset of coherence; at higher density, there is a different threshold behavior which occurs with weak coupling and can be identified with lasing; this transition occurs both with and without a trap

Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests

Copula mixed models for trivariate (or bivariate) meta-analysis of diagnostic test accuracy studies accounting (or not) for disease prevalence have been proposed in the biostatistics literature to synthesize information. However, many systematic reviews often include case-control and cohort studies, so one can either focus on the bivariate meta-analysis of the case-control studies or the trivariate meta-analysis of the cohort studies, as only the latter contains information on disease prevalence. In order to remedy this situation of wasting data we propose a hybrid copula mixed model via a combination of the bivariate and trivariate copula mixed model for the data from the case-control studies and cohort studies, respectively. Hence, this hybrid model can account for study design and also due to its generality can deal with dependence in the joint tails. We apply the proposed hybrid copula mixed model to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma

Estimating the Binary Endogenous Effect of Insurance on Doctor Visits by Copula-Based Regression Additive Models

This paper seeks to estimate the causal effect of having health insurance on health care utilization, while accounting for potential endogeneity bias. The topic has impor- tant policy implications, because health insurance reforms implemented in U.S. in recent decades have focused on extending coverage to the previously uninsured. Consequently, understanding the effects of those reforms requires an accurate estimate of the causal effect of insurance on utilization. However, obtaining such an estimate is complicated by the discreteness inherent in common measures of health care usage. This paper presents a flexible estimation approach, based on copula functions, that consistently estimates the coefficient of a binary endogenous regressor in count data settings. The relevant numeri- cal computations can be easily carried out using the freely available GJRM R package. The empirical results find significant evidence of favorable selection into insurance. Ignoring such selection, insurance appears to increase doctor visit usage by 62%, but adjusting for it, the effect increases to 134%

Strong Approximation of Empirical Copula Processes by Gaussian Processes

We provide the strong approximation of empirical copula processes by a Gaussian process. In addition we establish a strong approximation of the smoothed empirical copula processes and a law of iterated logarithm

A mixed effect model for bivariate meta-analysis of diagnostic test accuracy studies using a copula representation of the random effects distribution

Diagnostic test accuracy studies typically report the number of true positives, false positives, true negatives and false negatives. There usually exists a negative association between the number of true positives and true negatives, because studies that adopt less stringent criterion for declaring a test positive invoke higher sensitivities and lower specificities. A generalized linear mixed model (GLMM) is currently recommended to synthesize diagnostic test accuracy studies. We propose a copula mixed model for bivariate meta-analysis of diagnostic test accuracy studies. Our general model includes the GLMM as a special case and can also operate on the original scale of sensitivity and specificity. Summary receiver operating characteristic curves are deduced for the proposed model through quantile regression techniques and different characterizations of the bivariate random effects distribution. Our general methodology is demonstrated with an extensive simulation study and illustrated by re-analysing the data of two published meta-analyses. Our study suggests that there can be an improvement on GLMM in fit to data and makes the argument for moving to copula random effects models. Our modelling framework is implemented in the package CopulaREMADA within the open source statistical environment R

Signal Processing

Contains research objectives and reports on two research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 28-043-AMC-02536(E