DEVELOPING PREDICTION EQUATIONS FOR FAT FREE LEAN IN THE PRESENCE OF AN UNKNOWN AMOUNT OF PROPORTIONAL MEASUREMENT ERROR

Published prediction equations for fat-free lean mass are widely used by producers for carcass evaluation. These regression equations are commonly derived under the assumption that the predictors are measured without error. In practice, however, it is known that some predictors, such as backfat and loin muscle depth, are measured imperfectly with variance that is proportional to the mean. Failure to account for these measurement errors will cause bias in the estimated equation. In this paper, we describe an empirical Bayes approach, using technical replicates, to accurately estimate the regression relationship in the presence of proportional measurement error. We demonstrate, via simulation studies, that this Bayesian approach dramatically improves the accuracy of the estimated equation in comparison to the fit from Ordinary Least Squares regression

DEVELOPING PREDICTION EQUATIONS FOR CARCASS LEAN MASS IN THE PRESCENCE OF PROPORTIONAL MEASUREMENT ERROR

Published prediction equations for carcass lean mass are widely used by commercial pork producers for carcass valuation. These regression equations have been derived under the assumption that the predictors, such as back fat depth, are measured without error. In practice, however, it is known that these measurements are imperfect, with a variance that is proportional to the mean. In this paper, we consider both a linear and quadratic true relationship and compare regression fits among two methods that account for this error versus simply ignoring the additional error. We show that biased estimates of the relationship result if measurement error is ignored. Between our version of regression calibration and a Bayesian model approach, the Bayesian inference approach produced the least biased predictions. The benefits of our Bayesian approach also increased with an increase in the measurement error

Division of Credit Modeling for Team Sports with an Emphasis on NCAA Volleyball

Assessing player contribution in team sports has direct application to setting lineups and constructing team rosters. It also plays a big role in fan engagement, providing media content for talk show debates and opinion articles. Traditionally collected player contribution metrics have focused on a single aspect of game play and, as a result, haven’t captured the full contribution of a player. More recent metrics have been developed to more fully capture total contribution, placing players on a common basis of comparison. This dissertation proposes a general framework known as Division of Credit modeling for team sports. The purpose of which is to develop data driven approaches to value player contribution based on the apportioning of value from play outcomes. Many of its subcomponents can be found in the sports literature, but are presented here as a cohesive framework and applied directly to National Collegiate Athletic Association Women’s Volleyball. Volleyball is a generally underexplored sport in the literature for player evaluation, but has the necessary elements for a Division of Credit metric to make it a prime example. Models are presented to value contribution based on player presence, similar to the adjusted plus/minus, and to value contribution based on player action grades, a more thorough approach. The work concludes by describing extensions of these models to football

Integrated Environmental Assessment of Future Energy Scenarios Based on Economic Equilibrium Models

The future evolution of energy supply technologies strongly depends on (and affects) the economic and environmental systems, due to the high dependency of this sector on the availability and cost of fossil fuels, especially on the small regional scale. This paper aims at presenting the modeling system and preliminary results of a research project conducted on the scale of Luxembourg to assess the environmental impact of future energy scenarios for the country, integrating outputs from partial and computable general equilibrium models within hybrid Life Cycle Assessment (LCA) frameworks. The general equilibrium model for Luxembourg, LUXGEM, is used to evaluate the economic impacts of policy decisions and other economic shocks over the time horizon 2006-2030. A techno-economic (partial equilibrium) model for Luxembourg, ETEM, is used instead to compute operation levels of various technologies to meet the demand for energy services at the least cost along the same timeline. The future energy demand and supply are made consistent by coupling ETEM with LUXGEM so as to have the same macro-economic variables and energy shares driving both models. The coupling results are then implemented within a set of Environmentally-Extended Input-Output (EE-IO) models in historical time series to test the feasibility of the integrated framework and then to assess the environmental impacts of the country. Accordingly, a disaggregated energy sector was built with the different ETEM technologies in the EE-IO to allow hybridization with Life Cycle Inventory (LCI) and enrich the process detail. The results show that the environmental impact slightly decreased overall from 2006 to 2009. Most of the impacts come from some imported commodities (natural gas, used to produce electricity, and metalliferous ores and metal scrap). The main energy production technology is the combined-cycle gas turbine plant Twinerg, representing almost 80% of the domestic electricity production in Luxembourg. In the hybrid EE-IO model, this technology contributes to around 7% of the total impact of the country's net consumption. The causes of divergence between ETEM and LUXGEM are also thoroughly investigated to outline possible strategies of modeling improvements for future assessment of environmental impacts using EE-IO. Further analyses focus first on the completion of the models' coupling and its application to the defined scenarios. Once the coupling is consistently accomplished, LUXGEM can compute the IO flows from 2010 to 2030, while the LCI processes in the hybrid system are harmonized with ETEM to represent the future domestic and imported energy technologies