Browsing by Author "Lin, Ying"

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Comparing Prediction Methods of Wheat Grain Quality With the Area Under the Receiver Operating Characteristic Curves
(North Dakota State University, 2021) Lin, Ying
A widely used breeding method is genomic selection, which uses genome-wide marker coverage to predict genotypic values for quantitative traits. Genomic selection combines molecular and phenotypic data in a training population to obtain the genomic estimated breeding values of individuals in a testing population that have been genotyped but not phenotyped. One popular method for this estimation is G-BLUP. To further simplify data collection efforts and costs, we developed models with linear model, Bayesian linear model, K-nearest neighbors, and Random Forest to predict quality traits and compare the predictive ability of this new approach with G-BLUP using Pearson correlation and area under the receiver operating characteristic curve. The goal of this approach is to enable the analysis of large-scale data sets to provide relatively accurate estimates of quality traits without the time and energy consumption of marker analysis. Application of the methods to predict the quality traits for spring wheat breeding data reveals that compared with G-BLUP methods, the proposed methods perform better in loaf volume prediction, perform poorly in flour extraction and bake absorption prediction, and in mixograph prediction, the performance is not bad.
Testing Parallelism for the Four-Parameter Logistic Model with D-Optimal Design
(North Dakota State University, 2018) Lin, Ying
In order to determine the potency of the test preparation relative to the standard preparation, it is often important to test parallelism between a pair of dose-response curves of reference standard and test sample. Optimal designs are known to be more powerful in testing parallelism as compared to classical designs. In this study, D-optimal design was implemented to study the parallelism and compare its performance with a classical design. We modified Doptimal design to test the parallelism in the four-parameter logistic (4PL) model using Intersection-Union Test (IUT). IUT method is appropriate when the null hypothesis is expressed as a union of sets, and by using this method complicated tests involving several parameters are easily constructed. Since D-optimal design minimizes the variances of model parameters, it can bring more power to the IUT test. A simulation study will be presented to compare the empirical properties of the two different designs.