In the exact nonparametric illference, we frequelltly need the enumeration of all contingency tables with the same marginal totals. A qualltum jump toward a more rapid method took place with the publication of the net...In the exact nonparametric illference, we frequelltly need the enumeration of all contingency tables with the same marginal totals. A qualltum jump toward a more rapid method took place with the publication of the network approach of Mehta et al.[1-3]. It circumvents the need to explicitly enumerate each table and considerably extends the bounds of computational feasibility relative to the direct enumeration. In this paper) the present authors suggest another algorithm of implicit enumeration of all tables. First, a contingency table (T × c table or stratified 2×c table) is regarded as a number with several digits, and all contingency tables with the same marginal totals are regarded as a number sequence. A rule is then intyoduced to generate the sequence and to leap some subsequences which are useless for computing the probability values. The leaping subsequence aIgorithm proposed is more efficient than the network algorithm. Especially, when-the permutation distribution is required, our algorithm may run tens times faster than StatXact.[4]展开更多
文摘In the exact nonparametric illference, we frequelltly need the enumeration of all contingency tables with the same marginal totals. A qualltum jump toward a more rapid method took place with the publication of the network approach of Mehta et al.[1-3]. It circumvents the need to explicitly enumerate each table and considerably extends the bounds of computational feasibility relative to the direct enumeration. In this paper) the present authors suggest another algorithm of implicit enumeration of all tables. First, a contingency table (T × c table or stratified 2×c table) is regarded as a number with several digits, and all contingency tables with the same marginal totals are regarded as a number sequence. A rule is then intyoduced to generate the sequence and to leap some subsequences which are useless for computing the probability values. The leaping subsequence aIgorithm proposed is more efficient than the network algorithm. Especially, when-the permutation distribution is required, our algorithm may run tens times faster than StatXact.[4]
