Principal Component Analysis And Biochemical Characterization Of Protein And
Starch Reveal Primary Targets For Improving Sorghum Grain,
2010
University of California - Berkeley
Principal Component Analysis And Biochemical Characterization Of Protein And Starch Reveal Primary Targets For Improving Sorghum Grain, Joshua H. Wong, D. B. Marx, Jeff D. Wilson, Bob B. Buchanan, Peggy G. Lemaux, Jeffrey F. Pedersen
Department of Statistics: Faculty Publications
Limited progress has been made on genetic improvement of the digestibility of sorghum grain because of variability among different varieties. In this study, we applied multiple techniques to assess digestibility of grain from 18 sorghum lines to identify major components responsible for variability. We also identified storage proteins and enzymes as potential targets for genetic modification to improve digestibility. Results from principal component analysis revealed that content of amylose and total starch, together with protein digestibility (PD), accounted for 94% of variation in digestibility. Control of amylose content is understood and manageable. Up-regulation of genes associated with starch accumulation is …
Reference Priors For Exponential Families
With Increasing Dimension,
2010
University of Nebraska-Lincoln
Reference Priors For Exponential Families With Increasing Dimension, Bertrand S. Clarke, Subhashis Ghosal
Department of Statistics: Faculty Publications
In this article, we establish the asymptotic normality of the posterior distribution for the natural parameter in an exponential family based on independent and identically distributed data. The mode of convergence is expected Kullback-Leibler distance and the number of parameters p is increasing with the sample size n. Using this, we give an asymptotic expansion of the Shannon mutual information valid when p = pn increases at a sufficiently slow rate. The second term in the asymptotic expansion is the largest term that depends on the prior and can be optimized to give Jeffreys’ prior as the reference prior in …
Desiderata For A Predictive Theory Of Statistics,
2010
University of Miami
Desiderata For A Predictive Theory Of Statistics, Bertrand Clarke
Department of Statistics: Faculty Publications
In many contexts the predictive validation of models or their associated prediction strategies is of greater importance than model identification which may be practically impossible. This is particularly so in fields involving complex or high dimensional data where model selection, or more generally predictor selection is the main focus of effort. This paper suggests a unified treatment for predictive analyses based on six 'desiderata'. These desiderata are an effort to clarify what criteria a good predictive theory of statistics should satisfy.
