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    BLOCK DESIGNS UNDER AUTOCORRELATED ERRORS

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    Genre
    Thesis/Dissertation
    Date
    2011
    Author
    Shu, Xiaohua
    Advisor
    Raghavarao, Damaraju
    Committee member
    Iglewicz, Boris
    Chitturi, Pallavi
    Altan, Stanley
    Department
    Statistics
    Subject
    Statistics
    Autocorrelation
    Bib Designs
    Compound Symmetric Error Structure
    Orthogonal Arrays of Type I and Type II
    Pbib Designs
    Relative Efficiency
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2384
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2366
    Abstract
    This research work is focused on the balanced and partially balanced incomplete block designs when observations within blocks are correlated. The topic for this dissertation was motivated by a problem in pharmaceutical research, when several treatments are allocated to individuals, and repeated measurements are taken on each individual. In that case, there is correlation among the observations taken on the same individual. Typically, it is reasonable to assume that the observations within individual close to each other are highly correlated than observations that are far away from each other. It is also reasonable to assume that the correlation between any two observations within each individual is same. We have characterized balanced and partially balanced incomplete block designs when observations within blocks are autocorrelated. In Chapter 3, we have provided an explicit expression for the average variance of estimated elementary treatment contrasts for designs obtained by Type I and II series of orthogonal arrays, under autocorrelated errors, and compared them with the corresponding balanced incomplete block designs with uncorrelated errors. The relative efficiency of balanced incomplete block design compared to the corresponding balanced incomplete block design obtained by Types I and II series of orthogonal array under autocorrelated errors does not depend on the number of treatments (v) and is an increasing function of the block size (k). When orthogonal arrays of Type I or Type II do not exist for a given number of treatments, we provided alternative partially balanced designs with autocorrelated errors. In Chapter 4, we rearranged the treatments in each block of symmetric balanced incomplete block designs and used them with autocorrelated error structure of the plots in a block. The C-matrix of estimated treatment effects under autocorrelation was given and the relative efficiency of symmetric balanced incomplete block designs with independent errors compared to the autocorrelated designs is given. In Chapter 5, we discussed the compound symmetry correlation structure within blocks. An explicit expression of the average variance of designs obtained by Type I and II series of orthogonal arrays and symmetric balanced incomplete block designs under compound symmetric errors has been provided and compared them with the corresponding balanced incomplete block designs with uncorrelated errors. Finally, the relative efficiencies of these designs with autocorrelated errors vs. compound symmetric error structure are given
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