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, 1980 Some of the next articles are maybe not open access.
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Mixtures, embedding, and ancillarity
Canadian Journal of Statistics, 1985AbstractAncillary statistics, proposed by Fisher (1925), can be constructed by forming amixture model(Birnbaum 1962) or can be extracted or derived from atransformation‐parameter model(Peisakoff 1951, Fraser 1961) or from the corresponding error‐basedstructural model(Fraser 1968, 1979); these latter models involve an implicitmixturestructure.
Evans, M., Fraser, D. A. S., Monette, G.
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Ancillarity Principle and a Statistical Paradox
Journal of the American Statistical Association, 1982Abstract Among the many reasons underlying the practice of randomization some of the main ones can be described as averaging out or elimination of the effects of nuisance parameters. It is already well known (Godambe 1966) that averaging over all the possible results of the adopted randomization is directly in conflict with the likelihood principle ...
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On sufficiency and ancillarity in the presence of a nuisance parameter
Biometrika, 1980SUMMARY This paper discusses the definitions of ancillarity and sufficiency in the presence of a nuisance parameter given by Godambe (1976a). Illustrative examples are given and the relation to Fisher information discussed. In view of the properties of distribution functions which provide optimum estimating equations, Godambe (1976a) proposed ...
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Local ancillarity in the presence of a nuisance parameter
Biometrika, 1993Let \(S\) be a statistic with density depending on a scale parameter \(\theta\) and nuisance parameter \(\lambda\). If \(S\) can be written as \(S=(T,A)\), then \(A\) is ancillary for \(\theta\) in the presence of \(\lambda\) if the conditional distribution of \(T\) given \(A\) depends only on \(\theta\) and \(A\) contains ``no information about ...
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Biometrika, 1981
Let p(x, 6) be a probability density function with respect to some measure /, where x E E and 6 E Q, the sample and parameter space. Further, assume that 6 = (61, 62), 62 being a nuisance parameter, where 61 E Ql, 62 E Q2 and Q = Q1 X Q2. In this situation Godambe (1976) defined a statistic t as ancillary for estimating 61 if (a) the conditional ...
P. E. FERREIRA, Ch. E. MINDER
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Let p(x, 6) be a probability density function with respect to some measure /, where x E E and 6 E Q, the sample and parameter space. Further, assume that 6 = (61, 62), 62 being a nuisance parameter, where 61 E Ql, 62 E Q2 and Q = Q1 X Q2. In this situation Godambe (1976) defined a statistic t as ancillary for estimating 61 if (a) the conditional ...
P. E. FERREIRA, Ch. E. MINDER
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Biometrika, 1973
Eine parametrische Familie von Wahrscheinlichkeitsmaßen auf einer endlichen Menge - \(\mathfrak U\) heist universal, wenn für jeden Punkt \(u\in\mathfrak U\) ein Wert des Parameters \(u\) existiert, so daß für die Wahrscheinlichkeitsfunktion \(p(u,\omega)\) \(p(u,\omega)\geq p(\tilde u,\omega)\) \((\tilde u\in\mathfrak U)\) gilt.
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Eine parametrische Familie von Wahrscheinlichkeitsmaßen auf einer endlichen Menge - \(\mathfrak U\) heist universal, wenn für jeden Punkt \(u\in\mathfrak U\) ein Wert des Parameters \(u\) existiert, so daß für die Wahrscheinlichkeitsfunktion \(p(u,\omega)\) \(p(u,\omega)\geq p(\tilde u,\omega)\) \((\tilde u\in\mathfrak U)\) gilt.
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