Multivariate Statistical Modelling Based on Generalized Linear Models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1994
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introduction of generalized linear models (GLM) a much more flexible instrument for sta tistical modelling has been created. The broad class of GLM's includes some of the classicallinear models as special cases but is particularly suited for categorical discrete or nonnegative responses. The last decade has seen various extensions of GLM's: multivariate and multicategorical models have been considered, longitudinal data analysis has been developed in this setting, random effects and nonparametric pre dictors have been included. These extended methods have grown around generalized linear models but often are no longer GLM's in the original sense. The aim of this book is to bring together and review a large part of these recent advances in statistical modelling. Although the continuous case is sketched sometimes, thoughout the book the focus is on categorical data. The book deals with regression analysis in a wider sense including not only cross-sectional analysis but also time series and longitudinal data situations. We do not consider problems of symmetrical nature, like the investigation of the association structure in a given set of variables. For example, log-linear models for contingency tables, which can be treated as special cases of GLM's are totally omitted. The estimation approach that is primarily considered in this book is likelihood-based |
| Beschreibung: | 1 Online-Ressource (XXIV, 426 p) |
| ISBN: | 9781489900104 9781489900128 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4899-0010-4 |
Internformat
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| 650 | 4 | |a Mathematics | |
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Datensatz im Suchindex
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| any_adam_object | |
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| author_GND | (DE-588)120635682 (DE-588)172422973 |
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| author_role | aut |
| author_sort | Fahrmeir, Ludwig 1945- |
| author_variant | l f lf |
| building | Verbundindex |
| bvnumber | BV042421734 |
| classification_tum | MAT 000 |
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| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4899-0010-4 |
| format | Electronic eBook |
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| id | DE-604.BV042421734 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:09Z |
| institution | BVB |
| isbn | 9781489900104 9781489900128 |
| issn | 0172-7397 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027857151 |
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| physical | 1 Online-Ressource (XXIV, 426 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1994 |
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| publisher | Springer New York |
| record_format | marc |
| series2 | Springer Series in Statistics |
| spelling | Fahrmeir, Ludwig 1945- Verfasser (DE-588)120635682 aut Multivariate Statistical Modelling Based on Generalized Linear Models by Ludwig Fahrmeir, Gerhard Tutz New York, NY Springer New York 1994 1 Online-Ressource (XXIV, 426 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introduction of generalized linear models (GLM) a much more flexible instrument for sta tistical modelling has been created. The broad class of GLM's includes some of the classicallinear models as special cases but is particularly suited for categorical discrete or nonnegative responses. The last decade has seen various extensions of GLM's: multivariate and multicategorical models have been considered, longitudinal data analysis has been developed in this setting, random effects and nonparametric pre dictors have been included. These extended methods have grown around generalized linear models but often are no longer GLM's in the original sense. The aim of this book is to bring together and review a large part of these recent advances in statistical modelling. Although the continuous case is sketched sometimes, thoughout the book the focus is on categorical data. The book deals with regression analysis in a wider sense including not only cross-sectional analysis but also time series and longitudinal data situations. We do not consider problems of symmetrical nature, like the investigation of the association structure in a given set of variables. For example, log-linear models for contingency tables, which can be treated as special cases of GLM's are totally omitted. The estimation approach that is primarily considered in this book is likelihood-based Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf Lineares Modell (DE-588)4134827-8 gnd rswk-swf Multivariate Daten (DE-588)4195680-1 gnd rswk-swf Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd rswk-swf Multivariate Daten (DE-588)4195680-1 s Verallgemeinertes lineares Modell (DE-588)4124382-1 s 1\p DE-604 Multivariate Analyse (DE-588)4040708-1 s Lineares Modell (DE-588)4134827-8 s 2\p DE-604 Tutz, Gerhard 1950- Sonstige (DE-588)172422973 oth https://doi.org/10.1007/978-1-4899-0010-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Fahrmeir, Ludwig 1945- Multivariate Statistical Modelling Based on Generalized Linear Models Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Multivariate Analyse (DE-588)4040708-1 gnd Lineares Modell (DE-588)4134827-8 gnd Multivariate Daten (DE-588)4195680-1 gnd Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4134827-8 (DE-588)4195680-1 (DE-588)4124382-1 |
| title | Multivariate Statistical Modelling Based on Generalized Linear Models |
| title_auth | Multivariate Statistical Modelling Based on Generalized Linear Models |
| title_exact_search | Multivariate Statistical Modelling Based on Generalized Linear Models |
| title_full | Multivariate Statistical Modelling Based on Generalized Linear Models by Ludwig Fahrmeir, Gerhard Tutz |
| title_fullStr | Multivariate Statistical Modelling Based on Generalized Linear Models by Ludwig Fahrmeir, Gerhard Tutz |
| title_full_unstemmed | Multivariate Statistical Modelling Based on Generalized Linear Models by Ludwig Fahrmeir, Gerhard Tutz |
| title_short | Multivariate Statistical Modelling Based on Generalized Linear Models |
| title_sort | multivariate statistical modelling based on generalized linear models |
| topic | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Multivariate Analyse (DE-588)4040708-1 gnd Lineares Modell (DE-588)4134827-8 gnd Multivariate Daten (DE-588)4195680-1 gnd Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd |
| topic_facet | Mathematics Distribution (Probability theory) Probability Theory and Stochastic Processes Mathematik Multivariate Analyse Lineares Modell Multivariate Daten Verallgemeinertes lineares Modell |
| url | https://doi.org/10.1007/978-1-4899-0010-4 |
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