Prescott, Philip and Mansson, Ralph
Robustness of balanced incomplete block designs to randomly missing observations
Journal of Statistical Planning and Inference, 92, (1-2), . (doi:10.1016/S0378-3758(00)00147-6).
Full text not available from this repository.
Practical experimenters must always be aware of the possibility that some of their observations could become unavailable for analysis. In an experiment involving treatments and blocks, it could be desirable to select a design that is resistant to the loss of a complete block or treatment, or a small number of observations distributed at random throughout the initial design. In this paper, we examine the robustness of binary, variance-balanced, incomplete block designs using the eigenvalues of the associated information matrix when specific observations are missing. Results are presented for up to three missing observations and the procedure is illustrated using an example involving eight treatments arranged in 14 blocks of four treatments per block. On the basis of these considerations, it is recommended that, to guard against a substantial loss of efficiency due to a small number of randomly missing observations, it is preferable to use designs with as few treatments common to pairs of blocks as possible.
Actions (login required)