<電子ブック>
Applying Generalized Linear Models / by James K. Lindsey
(Springer Texts in Statistics. ISSN:21974136)
版 | 1st ed. 1997. |
---|---|
出版者 | (New York, NY : Springer New York : Imprint: Springer) |
出版年 | 1997 |
本文言語 | 英語 |
大きさ | XIV, 256 p : online resource |
著者標目 | *Lindsey, James K author SpringerLink (Online service) |
件 名 | LCSH:Probabilities LCSH:Statistics LCSH:Biometry FREE:Probability Theory FREE:Statistical Theory and Methods FREE:Biostatistics |
一般注記 | Generalized Linear Modelling: Statistical Modelling -- Exponential Dispersion Models -- Linear Structure -- Three Components of a GLM -- Possible Models -- Inference -- Exercises. Discrete Data: Log Linear Models -- Models of Change -- Overdispersion -- Exercises. Fitting and Comparing Probability Distributions: Fitting Distributions -- Setting Up the Model -- Special Cases -- Exercises. Growth Curves: Exponential Growth Curves -- Logistic Growth Curve -- Gomperz Growth Curve -- More Complex Models -- Exercises. Time Series: Poisson Processes -- Markov Processes -- Repeated Measurements -- Exercises. Survival Data: General Concepts -- 'Nonparametric' Estimation -- Parametric Models -- 'Semiparametric' Models -- Exercises. Event Histories: Event Histories and Survival Distributions -- Counting processes -- Modelling Event Histories -- Generalizations -- Exercises. Spatial data: Spatial Interaction -- Spatial Patterns -- Exercises. Normal Models: Linear Regression -- Analysis of Variance -- Nonlinear Regression -- Exercises. Dynamic Models: Dynamic Generalized Linear Models -- Normal Models -- Count Data -- Positive Response Data -- Continuous Time Nonlinear Models. Appendices: Inference -- Diagnostics -- References -- Index Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Diepenbeek, in the social science department at the University of Liège, and in medical statistics at DeMontfort University, Leicester. He is the author of nine other books HTTP:URL=https://doi.org/10.1007/b98856 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
Springer eBooks | 9780387227306 |
|
電子リソース |
|
EB00227376 |
書誌詳細を非表示
データ種別 | 電子ブック |
---|---|
分 類 | LCC:QA273.A1-274.9 DC23:519.2 |
書誌ID | 4000104543 |
ISBN | 9780387227306 |
類似資料
この資料の利用統計
このページへのアクセス回数:2回
※2017年9月4日以降