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Full-Text Articles in Physical Sciences and Mathematics
Developing Prediction Equations For Fat Free Lean In The Presence Of An Unknown Amount Of Proportional Measurement Error, Zachary J. Hass, Bruce A. Craig, Allan Schinckel
Developing Prediction Equations For Fat Free Lean In The Presence Of An Unknown Amount Of Proportional Measurement Error, Zachary J. Hass, Bruce A. Craig, Allan Schinckel
Conference on Applied Statistics in Agriculture
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 …
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Developing Prediction Equations For Carcass Lean Mass In The Prescence Of Proportional Measurement Error, Zachary J. Hass, Ziqi Zhou, Bruce A. Craig
Conference on Applied Statistics in Agriculture
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 …
A Non-Parametric Empirical Bayes Approach For Estimating Transcript Abundance In Un-Replicated Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge
A Non-Parametric Empirical Bayes Approach For Estimating Transcript Abundance In Un-Replicated Next-Generation Sequencing Data, Sanvesh Srivastava, R. W. Doerge
Conference on Applied Statistics in Agriculture
Empirical Bayes approaches have been widely used to analyze data from high throughput sequencing devices. These approaches rely on borrowing information available for all the genes across samples to get better estimates of gene level expression. To date, transcript abundance in data from next generation sequencing (NGS) technologies has been estimated using parametric approaches for analyzing count data, namely – gamma-Poisson model, negative binomial model, and over-dispersed logistic model. One serious limitation of these approaches is they cannot be applied in absence of replication. The high cost of NGS technologies imposes a serious restriction on the number of biological replicates …