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# Some Comments on Philatelic Latin Squares from Pakistan

Some Comments on Philatelic Latin Squares from Pakistan

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440 ong>Someong> ong>Commentsong> on Philatelic Latin Squares from Pakistan The Knut Vik designs KV1 and KV2 are also A-efficient 10 and pandiagonal, in that all diagonals (the two main diagonals and all broken diagonals) contain each of the five treatments precisely once. Moreover no two diagonally-adjacent cells have the same treatment and, as noted by Bailey, Kunert & Martin [6], KV1 and KV2 are the only gerechte designs with this property. As Bailey, Cameron & Connelly [4] point out, in 1956 [10] Walter- Ulrich Behrens 11 (1902–1962) introduced a specialization of Latin squares which he called “gerechte” (Figure 4.2). Behrens [10, Tabelle 5b, p. 178] refers to the Latin square GD2 as a “diagonal layout” (German: Diagonalanordnung)—all the elements on the main backwards diagonal are equal (to 1), and [10, Tabelle 5a, p. 178] calls KV1 a “knight’s move layout” (German: Rösselsprunganordnung) and KV2 [10, Tabelle 8, p. 182] a “gerechte Latin square” (German: gerechte lateinische Quadrate). FIGURE 4.2: Gerechte Latin squares GD1 (left panel) and GD2 (right panel). In a k × k “gerechte design” the k × k grid is partitioned into k “regions” S1,S2,...,Sk, say, each containing k cells of the grid; we are required to place the symbols 1,2,...,k into the cells of the grid in such a way that each symbol occurs once in each row, once in each column, and once in each region. The row and column constraints say that the solution is a Latin square, and the last constraint restricts the possible Latin squares. Gerechte designs originated in the statistical design of agricultural experiments, where they ensure that treatments are fairly exposed to localized variations in the field containing the experimental plots. The Knut Vik designs KV1 and KV2 are gerechte designs of types GD1 and GD2, respectively. Bailey, Kunert & Martin [5, (1990)] point out that 10 The A-criterion in the analysis of variance with a Latin square design is equivalent to minimizing: (i) the average variance of treatment-effect estimates, (ii) the average variance of an estimated difference in the treatmenteffect estimates, and (iii) the expected value of the treatment sum of squares when there are no treatment differences [55, p. 251]. 11 The well-known Behrens–Fisher problem of comparing means of two populations with possibly unequal variances was first considered by this Walter-Ulrich Behrens [8] and Sir Ronald Aylmer Fisher (1890–1962) [39,40]. For recent results on the Behrens–Fisher problem see Ruben [69].

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