“…Namely, in [17] the authors obtained lower and upper bounds for the optimal A2 configuration leaving the exact formulae undiscovered. In this paper, we fill in the gap by providing exact solution similar to that obtained by Samuels [31] and Skorniakov and Čižikovienė [44] for the case of classical Dorfman scheme and its modifications.…”
Section: Introductionmentioning
confidence: 92%
“…For the case of the Dorfman scheme described above, the optimal configuration was established by Samuels [31] quite long ago. However, for a couple of its modifications, namely, the modified Dorfman scheme [33] and Sterrett scheme [34], the analytical formulae, though conjectured, were unknown [24] and established only recently [44]. Pretty much the same situation is with A2.…”
Up to date, only lower and upper bounds for the optimal configuration of a Square Array (A2) Group Testing (GT) algorithm are known. We establish exact analytical formulae and provide a couple of applications of our result. First, we compare the A2 GT scheme to several other classical GT schemes in terms of the gain per specimen attained at optimal configuration. Second, operating under objective Bayesian framework with the loss designed to attain minimum at optimal GT configuration, we suggest the preferred choice of the group size under natural minimal assumptions: the prior information regarding the prevalence suggests that grouping and application of A2 is better than individual testing. The same suggestion is provided for the Minimax strategy.
“…Namely, in [17] the authors obtained lower and upper bounds for the optimal A2 configuration leaving the exact formulae undiscovered. In this paper, we fill in the gap by providing exact solution similar to that obtained by Samuels [31] and Skorniakov and Čižikovienė [44] for the case of classical Dorfman scheme and its modifications.…”
Section: Introductionmentioning
confidence: 92%
“…For the case of the Dorfman scheme described above, the optimal configuration was established by Samuels [31] quite long ago. However, for a couple of its modifications, namely, the modified Dorfman scheme [33] and Sterrett scheme [34], the analytical formulae, though conjectured, were unknown [24] and established only recently [44]. Pretty much the same situation is with A2.…”
Up to date, only lower and upper bounds for the optimal configuration of a Square Array (A2) Group Testing (GT) algorithm are known. We establish exact analytical formulae and provide a couple of applications of our result. First, we compare the A2 GT scheme to several other classical GT schemes in terms of the gain per specimen attained at optimal configuration. Second, operating under objective Bayesian framework with the loss designed to attain minimum at optimal GT configuration, we suggest the preferred choice of the group size under natural minimal assumptions: the prior information regarding the prevalence suggests that grouping and application of A2 is better than individual testing. The same suggestion is provided for the Minimax strategy.
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