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作者：MCCULLAGH, P
作者单位：University of London; Imperial College London
摘要：A parametric model is developed for the analysis of square contingency tables with ordered categories. Order among the categories is a built-in feature of the new model and it is unnecessary to assign arbitrary scores to the row and column variables. Special cases of the proposed model include conditional symmetry and symmetry. The relationship with marginal homogeneity is described. The model for quasisymmetry is considered and is invariant under general permutation transformations of the ind...
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作者：BRYANT, P; WILLIAMSON, JA
作者单位：International Business Machines (IBM); IBM USA; University of Colorado System; University of Colorado Boulder
摘要：Maximum likelihood techniques as applied to classification and clustering problems are examined, and the classification maximum likelihood technique, in which individual observations are assigned on an all-or-nothing basis to 1 of several classes as part of the maximization process, is shown to give results asymptotically biased. This extends Marriott''s work for normal component distributions. Numerical examples are presented for normal component distributions and for a problem in genetics. B...
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作者：GLAZEBROOK, KD
摘要：A model for the allocation problem in a controlled clinical trial is proposed and is more general than the two-armed bandit. The trial involves more than 2 treatments and experiments to have a large set of possible outcomes. The optimal strategy for the allocation problem is straightforward to compute.
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作者：GAIL, M; WARE, J
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
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作者：BLUNCK, M; MOMMSEN, TP
摘要：Systematic errors caused by some methods of estimating the parameters of a rectangular hyperbola are discussed using a 2nd-order Taylor expansion. Effects of nontrivial weighting factors, sample size and types of error in the data are analyzed.
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作者：LAGAKOS, SW; SOMMER, CJ; ZELEN, M
摘要：Nonparametric likelihood methods are developed for the analysis of partially censored data arising from a multistate stochastic process. It is assumed that the underlying process follows a semi-Markov model in which state changes form an embedded Markov chain and sojourn times are independent with distributions depending only on adjoining states. The general likelihood function for a set of partially censored observations is determined and maximized nonparametrically. The resulting nonparametr...
