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Titlebook: Generalized Linear Models; Proceedings of the G Robert Gilchrist,Brian Francis,Joe Whittaker Conference proceedings 1985 Springer-Verlag Be

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發(fā)表于 2025-3-21 18:12:21 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Generalized Linear Models
副標(biāo)題Proceedings of the G
編輯Robert Gilchrist,Brian Francis,Joe Whittaker
視頻videohttp://file.papertrans.cn/383/382222/382222.mp4
叢書名稱Lecture Notes in Statistics
圖書封面Titlebook: Generalized Linear Models; Proceedings of the G Robert Gilchrist,Brian Francis,Joe Whittaker Conference proceedings 1985 Springer-Verlag Be
出版日期Conference proceedings 1985
關(guān)鍵詞Fitting; Generalized linear model; Latent Class Analysis; Likelihood; Variance; best fit
版次1
doihttps://doi.org/10.1007/978-1-4615-7070-7
isbn_softcover978-0-387-96224-5
isbn_ebook978-1-4615-7070-7Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 1985
The information of publication is updating

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,GLIM4 — Directions for Development, error distributions, which should include general mixture distributions fitted by nonparametric maximum likelihood, by the enhancement of the programming language to include matrix and array operations and eigenvalue-eigenvector decompositions, and by the enhancement of its present graphics facilit
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地板
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Comparing Estimated Spectral Densities Using GLIM,hmic link. This provides an immediate extension to the case of p > 2 spectra. GLIM macros for implementing the test are listed. An application to some medical data concerning natural fluctuations in the level of luteinising hormone in blood-samples is described.
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發(fā)表于 2025-3-22 12:33:22 | 只看該作者
Semi-parametric Generalized Linear Models,uandary. Should parametric models be abandoned altogether, thus losing the opportunity of estimating parameters of real interest and sacrificing efficiency in estimation and prediction, or should the extraneous variables be forced into a parametric model by imposing a possibly inappropriate function
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Generalized Additive Models; Some Applications,formation of the posterior probability p(y=l/ .) in logistic regression. In fact, these models generalize the whole family of GLIM models η(.)=. where η(.)=g(μ(.)) is some transformation of the regression function. We use the local scoring algorithm to estimate the functions, which uses a scatterplo
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