Titlebook: Advances in the Statistical Sciences: Foundations of Statistical Inference; Volume II of the Fes Ian B. MacNeill,Gary J. Umphrey,Serge B. P

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書目名稱Advances in the Statistical Sciences: Foundations of Statistical Inference影響因子(影響力)學(xué)科排名




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書目名稱Advances in the Statistical Sciences: Foundations of Statistical Inference讀者反饋學(xué)科排名

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Statistical Principles and Tangent Models,of sufficiency and conditionality is an underlying intention of separating . from .. This manifests itself in the ordinary analysis of variance and also for the generalizations of that analysis for the transformation models and for the exponential models. This paper gives preliminary introduction to

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Bernoulli Pairs with Invariant Reversals: An Example of Partial Likelihood,interest. The likelihood factorizes and the partial likelihood depends on. alone. This paper examines the problem of making inferences about. in detail and concludes that although the partial likelihood method is asymptotically sound the approach to the limit is slow, so that the method can lose a l

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描述

Statistical Inference for the Overlap Hypothesis,n between .. and the gain in height, .. - .., or that the correlation between .. and .. is the same as that between .. and .. - ... An estimate of the correlation when either of these hypotheses holds is obtained. A statistical test for the hypothesis is also developed, and indications of a multivar

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粗俗人

An Algorithm for Concave Regression,mputation of the maximum likelihood (normality) or least squares fit is available for the isotonic case in a bounded number of steps. For the concave case most of the algorithms have been difficult to implement or had no assured convergence; Dykstra (1983) gave an algorithm easy to implement and con

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On the Prediction of the Difference between Responses from Two Linear Models, transformation, the prediction distributions of the differences between responses are obtained. The usual method of derivation involves a cumbersome integration procedure which is avoided here by the use of structural relations.

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On Ultrastructural Relationships Models,s models. Dolby (1976) combined these two models in the univariate case into a single model called the ultrastructural relationships model and found the maximum likelihood estimators of the parameters. We extend Dolby’s ultrastructural relationships model to the multivariate case, assuming the exist