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Efficiency of NNBD and NNBIBD using autoregressive model

Abstract

Neighbour Balanced Block Designs, permitting the estimation of direct and neighbour effects, are used when the treatment applied to one experimental plot may affect the response on neighbouring plots as well as the response on the plot to which it is applied. The allocation of treatments in these designs is such that every treatment occurs equally often with every other treatment as neighbours. Neighbour Balanced Block Designs for observations correlated within a block have been investigated for the estimation of direct as well as left and right neighbour effects of treatments. It is observed that efficiency for direct as well as neighbour effects is high, in case of Complete block designs i.e. m = 0 for Nearest Neighbour correlation structure with ρ in the interval 0.1 to 0.7. In case of incomplete block designs m = 1,2,…, v-4 for Nearest Neighbour correlation structure turns out to be more efficient with ρ in the interval 0.1 to 0.7 using AR (1) model.

Optimal Experimental Design MethodsAdvanced Statistical Methods and ModelsBlock (permutation group theory)Nearest neighbourMathematicsAutoregressive modelInterval (graph theory)StatisticsBlock designCombinatoricsComputer scienceArtificial intelligence
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Efficiency of NNBD and NNBIBD using autoregressive model · Scinovex