株式会社極東書店トップ商品一覧Generalized Linear Models : Proceedings of the GLIM 85 Conference held at Lancaster, UK, Sept. 16-19, 1985. Softcover reprint of the original 1st ed. 1985.

商品詳細

Generalized Linear Models

Generalized Linear Models : Proceedings of the GLIM 85 Conference held at Lancaster, UK, Sept. 16-19, 1985. Softcover reprint of the original 1st ed. 1985.

・ISBN 978-0-387-96224-5 paper EUR 99.99

¥26,726.- (税込) (※)価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。

お気に入り
著者・編者Gilchrist, Robert / Francis, Brian / Whittaker, Joe (eds.),
シリーズLecture Notes in Statistics
出版社(Springer-Verlag New York Inc., US)
出版年月1985
ページ数182 pp.
言語ENG
ニュース番号<M25-18433>

解説

This volume consists of the published proceedings of the GLIM 95 Conference, held at Lancaster University, UK, from 16-19 September 1995. This is the second of such proceedings, the first of which was published as No 14 of the Springer-Verlag Lecture Notes in Statistics (Gilchrist,ed,1992). Since the 1992 conference there has been a modest update of the GLIM system, called GLIM 3.77. This incorporates some minor but pleasant enhancements and these are outlined in these proceedings by payne and Webb. With the completion of GLIM 3.77, future developments of the GLIM system are again under active review. Aitkin surveys possible directions for GLIM. one sOlMlWhat different avenue for analysing generalized linear models is provided by the GENSTAT system; Lane and payne discuss the new interactive facilities p~ided by version 5 of GENSTAT. On the theory Side, NeIder extends the concept and use of quasi-likelihood, giving useful forms of variance function and a method of introducing a random element into the linear predictor. Longford discusses one approach to the analysis of clustered observations (subjects within groups). Green and Yandell introduce 'semi-parametric modelling', allowing a compromise between parametriC and non-parametriC modelling. They modify the linear predictor by the addition of a ( smooth) curve, and estimate parameters by maximising a penalised log-likelihood. Hastie and Tibshirani introduce generalized additive models, introducing a linear predictor of the form 11 = (X + Efj(xj), with the fj estimated from the data by a weighted average of neighbouring observations.