TY - JOUR T1 - ON THE GENERIC CUT–POINT DETECTION PROCEDURE IN THE BINOMIAL GROUP TESTING AU - Skorniakov, Viktor AU - Cizikoviene, Ugne PY - 2025 DA - September Y2 - 2024 JF - TWMS Journal of Applied and Engineering Mathematics JO - JAEM PB - Işık University Press WT - DergiPark SN - 2146-1147 SP - 2366 EP - 2379 VL - 15 IS - 9 LA - en AB - Initially announced by Dorfman in 1943 for medical screening, (Binomial) Group Testing (BGT) was quickly recognized as a useful probabilistic tool in many other fields: quality control, communications and networking, engineering, statistics, etc. To apply any particular BGT procedure effectively, one first of all needs to know an important operating characteristic, the so called Optimal Cut-Point (OCP), describing the limits of its applicability. The determination of the latter is often a complicated task. In this work, we provide a generic algorithm suitable for a wide class of the BGT procedures and demonstrate its applicability by example. The way we do it exhibits independent interest since we link the BGT to seemingly unrelated field — the bifurcation theory. KW - Group Testing KW - Cut–Point KW - Bifurcation Theory. CR - Reference1 Aldridge, M., Johnson, O., and Scarlett, J., (2019), Group testing: an information theory perspective, Foundations and Trends in Communications and Information Theory Series, Now Publishers. CR - Reference2 Berger, T., Mandell, J. W., Subrahmanya, P., (2000), Maximally efficient two-stage screening, Bio- metrics, 56(3), pp. 833-840. CR - Reference3 PBC Desmos Studio, (2023), Desmos graphing calculator, https://www.desmos.com/calculator, On- line, Accessed March 2023. CR - Reference4 Dorfman, R., (1943), The detection of defective members of large populations, The Annals of Math- ematical Statistics, 14(4), pp. 436-440. CR - Reference5 Hudgens, M. G., Kim, H. Y., (2011), Optimal configuration of a square array group testing algorithm, Communications in Statistics - Theory and Methods, 40(3), pp. 436-448. CR - Reference6 Johnson, N. L., Kotz, S., Xizhi, W., (1991), Inspection errors for attributes in quality control, Springer US, Boston, MA. CR - Reference7 Malinovsky, Y., Albert, P. S., (2019), Revisiting nested group testing procedures: new results, com- parisons, and robustness, The American Statistician, 73(2), pp. 117-125. CR - Reference8 Phatarfod, R. M., Sudbury, A., (1994), The use of a square array scheme in blood testing, Statistics in Medicine, 13(22), pp. 2337–2343. CR - Reference9 Samuels, S. M., (1978), The exact solution to the two-stage group-testing problem, Technometrics, 20(4), pp. 497-500. CR - Reference10 Sobel, M., Groll, P. A., (1959) Group testing to eliminate efficiently all defectives in a binomial sample, Bell System Technical Journal, 38(5), pp. 1179-1252. CR - Reference11 Sterrett, A., (1957), On the detection of defective members of large populations, The Annals of Mathematical Statistics, 28(4), pp. 1033-1036. CR - Reference12 Strogatz, S. H., (2015), Nonlinear dynamics and chaos: with applications to physics, biology, chem- istry, and engineering, Second edition, Westview Press. CR - Reference13 Ungar, P., (1960), The cutoff point for group testing, Communications on Pure and Applied Mathe- matics, 13(1), pp. 49-54. CR - Reference14 Virtanen, P., et al., (2020), SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nature Methods, 17(3), pp. 261-272. CR - Reference15 Yao, Y. C., Hwang, F. K., (1988), A fundamental monotonicity in group testing, SIAM Journal on Discrete Mathematics, 1(2), pp. 256-259. CR - Reference16 Yao, Y. C., Hwang, F. K., (1990), On optimal nested group testing algorithms, Journal of Statistical Planning and Inference, 24(2), pp. 167-175. UR - https://dergipark.org.tr/en/pub/twmsjaem/issue//1792357 L1 - https://dergipark.org.tr/en/download/article-file/5279940 ER -