Flexible Regression and Smoothing (Record no. 4878)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02421 a2200313 4500 |
| 001 - CONTROL NUMBER | |
| control field | 1351980378 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250317111610.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250312042017xx 328 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781351980371 |
| 037 ## - SOURCE OF ACQUISITION | |
| Source of stock number/acquisition | Taylor & Francis |
| Terms of availability | GBP 47.99 |
| Form of issue | BB |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | 01 |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | KCH |
| Source | thema |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | PBT |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | KCHS |
| Source | bic |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | MAT029000 |
| Source | bisac |
| 072 7# - SUBJECT CATEGORY CODE | |
| Subject category code | 519.536028553 |
| Source | bisac |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mikis D. Stasinopoulos |
| 245 10 - TITLE STATEMENT | |
| Title | Flexible Regression and Smoothing |
| Remainder of title | Using GAMLSS in R |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Name of publisher, distributor, etc. | Chapman and Hall/CRC |
| Date of publication, distribution, etc. | 20170421 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 571 p |
| 520 ## - SUMMARY, ETC. | |
| Expansion of summary note | This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables. Key Features: Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R. Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning. R code integrated into the text for ease of understanding and replication. Supplemented by a website with code, data and extra materials. This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Robert A. Rigby |
| Relationship | A01 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Gillian Z. Heller |
| Relationship | A01 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Vlasios Voudouris |
| Relationship | A01 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Fernanda De Bastiani |
| Relationship | A01 |
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