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Titlebook: Statistical Modelling; Proceedings of the 1 Gilg U. H. Seeber,Brian J. Francis,Gabriele Stecke Conference proceedings 1995 Springer Science

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书目名称Statistical Modelling
副标题Proceedings of the 1
编辑Gilg U. H. Seeber,Brian J. Francis,Gabriele Stecke
视频video
丛书名称Lecture Notes in Statistics
图书封面Titlebook: Statistical Modelling; Proceedings of the 1 Gilg U. H. Seeber,Brian J. Francis,Gabriele Stecke Conference proceedings 1995 Springer Science
描述This volume presents the published proceedings of the lOth International Workshop on Statistical Modelling, to be held in Innsbruck, Austria from 10 to 14 July, 1995. This workshop marks an important anniversary. The inaugural workshop in this series also took place in Innsbruck in 1986, and brought together a small but enthusiastic group of thirty European statisticians interested in statistical modelling. The workshop arose out of two G LIM conferences in the U. K. in London (1982) and Lancaster (1985), and from a num­ ber of short courses organised by Murray Aitkin and held at Lancaster in the early 1980s, which attracted many European statisticians interested in Generalised Linear Modelling. The inaugural workshop in Innsbruck con­ centrated on GLMs and was characterised by a number of features - a friendly and supportive academic atmosphere, tutorial sessions and invited speakers presenting new developments in statistical modelling, and a very well organised social programme. The academic programme allowed plenty of time for presentation and for discussion, and made available copies of all papers beforehand. Over the intervening years, the workshop has grown substantially, and
出版日期Conference proceedings 1995
关键词Branching process; Fitting; Generalized linear model; Likelihood; Markov chain; Time series; correlation; l
版次1
doihttps://doi.org/10.1007/978-1-4612-0789-4
isbn_softcover978-0-387-94565-1
isbn_ebook978-1-4612-0789-4Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer Science+Business Media New York 1995
The information of publication is updating

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NPML estimation of the mixing distribution in general statistical models with unobserved random effmodels. These developments extend the standard methods of generalized linear modelling to deal with overdispersion and variance component structures caused by the presence of . in the models. The value of these methods is that they are not restricted by particular statistical model assumptions about
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Autoregressive Modelling of Markov Chains,regressive modelling which utilizes a same transition matrix for every lag. In this paper, we show that a model of the same type, but utilizing different matrices, gives best results and is not harder to estimate, even when the number of data is small.
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,A Case—Study on Accuracy of Cytological Diagnosis,al tumours. A 100 slides set standard was read by 16 raters; the majority diagnosis has been defined as the modal rating for each slide, and the target diagnosis was known by histopathology and clinical follow-up..In the present paper we analyse ratings from seven raters on a dichotomous classificat
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