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Yehua Li, Iowa State University

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Statistical Inference for Nested Hierarchical Functional Data
06 April 2017 from 4:00 PM to 5:00 PM
201 Thomas Bld
Contact Name
Lorey Burghard
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In a plant science Root Image Study, the process of seedling roots bending in response to gravity is recorded using digital cameras, and the bending rates are modeled as functional plant phenotype data. The functional phenotypes are collected from seeds representing a large variety of genotypes and have a three-level nested hierarchical structure, with seeds nested in groups nested in genotypes. The seeds are imaged on different days of the lunar cycle, and an important scientific question is whether there are lunar effects on root bending. We allow the mean function of the bending rate to depend on the lunar day and model the phenotypic variation between genotypes, groups of seeds imaged together, and individual seeds by hierarchical functional random effects. We estimate the covariance functions of the functional random effects by a fast penalized tensor product spline approach, perform multi-level functional principal component analysis (FPCA) using the best linear unbiased predictor of the principal component scores, and improve the efficiency of mean estimation by iterative decorrelation. We choose the number of principal components using a conditional Akaike Information Criterion and test the lunar day effect using generalized likelihood ratio test statistics based on the marginal and conditional likelihoods. We also propose a permutation procedure to evaluate the null distribution of the test statistics. Our simulation studies show that our model selection criterion selects the correct number of principal components with remarkably high frequency, and the likelihood-based tests based on FPCA have higher power than a test based on working independence.

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