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    Growth Curve Analysis and Change-Points Detection in Extremes

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    Type
    Thesis
    Authors
    Meng, Rui cc
    Advisors
    Sun, Ying cc
    Committee members
    Genton, Marc G. cc
    Huser, Raphaël cc
    Tester, Mark A. cc
    Program
    Applied Mathematics and Computational Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2016-05-15
    Permanent link to this record
    http://hdl.handle.net/10754/609833
    
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    Abstract
    The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed for detecting single and multiple change-points, and their performance is compared by simulation studies. The proposed method is illustrated using sea surface temperature data and the tail distributions before and after the change-point from two models, with and without random effects are compared.
    Citation
    Meng, R. (2016). Growth Curve Analysis and Change-Points Detection in Extremes. KAUST Research Repository. https://doi.org/10.25781/KAUST-85F27
    DOI
    10.25781/KAUST-85F27
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-85F27
    Scopus Count
    Collections
    Applied Mathematics and Computational Science Program; MS Theses; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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