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General Block Designs and Their Statistical Properties

  • Tadeusz Caliński
  • Sanpei Kageyama
Part of the Lecture Notes in Statistics book series (LNS, volume 150)

Abstract

According to one of the basic principles of experimental design, the randomiza-tion principle (see Section 1.1), the experimental units (plots) are to be random-ized before they enter the experiment. Suppose that to apply a general block design, in the sense of Section 2.2, randomization is performed as described by Nelder (1954), i.e., by randomly permuting blocks within a total area of them and by randomly permuting units within the blocks. Then, assuming the usual unit-treatment additivity (in the sense of Neider, 1965b, p. 168; see also White, 1975, p. 560; Bailey, 1981, p. 215, 1991, p. 30; Kala, 1991, p. 7; Hinkelmann and Kempthorne, 1994, p. 251), and, as usual, that the technical errors are uncorrelated, each with zero expectation and a finite variance, and that they are independent of the unit responses to treatments (see Neyman, 1935, pp. 110-114 and 145; Kempthorne, 1952, p. 132 and Section 8.4; Ogawa, 1961, 1963; Hinkelmann and Kempthorne, 1994, Section 9.2.6), the model of the variables observed on the n units actually used in the experiment can be written in matrix notation as in (1.3.19), i.e., as
$$ y = \Delta '\tau + D'\beta + \eta + e, $$
(3.1.1)
where y is an n x 1 vector of observed variables, T is a v x 1 vector of treatment parameters, β is a b x 1 vector of block random effects, η is an n x 1 vector of unit errors and e is an n x 1 vector of technical errors, the matrices Δ’ and D’ being defined as in Section 2.2. Properties of the model (3.1.1) can be established by following its derivation from the randomizations involved, as it has been performed for randomized blocks, the classic RBD, in Section 1.3.

Keywords

Block Design Incidence Matrix Proper Design Efficiency Factor Stratum Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Tadeusz Caliński
    • 1
  • Sanpei Kageyama
    • 2
  1. 1.Department of Mathematical and Statistical MethodsAgricultural University of PoznańPoznańPoland
  2. 2.Department of Mathematics, Faculty of School EducationHiroshima UniversityHigashi-Hiroshima 739Japan

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