J. I. Marden. Analyzing and Modeling Rank Data. Monographs on Statistics and Applied Probability,No64, Chapman & Hall, 1995. Home/Search Document Not in Database Summary Related Articles Check |
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Rank Aggregation Methods for the Web - Dwork, Kumar, Naor, Sivakumar (2001) (36 citations) (Correct)
....of experimental results will be in terms of these distance measures. While these distance measures seem natural, why these measures are good is moot. We do not delve into such discussions here# the interested reader can find such arguments in the books by Diaconis [12] Critchlow [11] or Marden [17]. 2.1.2 Optimal rank aggregation In the generic context of rank aggregation, the notion of better depends on what distance measure we strivetooptimize. Suppose we wish to optimize Kendall distance, the question then is: given (full or partial) lists 1#: # k ,find a oe such that oe is a ....
J. I. Marden. Analyzing and Modeling Rank Data. Monographs on Statistics and Applied Probability,No64, Chapman & Hall, 1995.
Analysis of systematic scan Metropolis algorithms using.. - Diaconis, Ram (2000) (1 citation) (Correct)
....by Mallows [Ma] for the study of permutations. He used the length function as a distance, x 1 x 0 ) d(x; x 0 ) and estimated q and x 0 to match data. Such Mallows models have had application and development for ranked and partially ranked data using a variety of metrics [D] Cr] FV] [Mar]. They have also been used for phylogenetic trees [BHV] classi cation trees [SB] and compositions [Det] One problem in studying Mallows models is that the normalizing constant P X (q) is uncomputable in general. In such cases properties of can be studied by simulation using the Metropolis ....
J. Marden, Analyzing and modeling rank data, Monographs on Statistics and Applied Probability 64, Chapman & Hall, London, 1995.
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