hardcover. Condizione: Very Good. Cover and edges may have some wear.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 142,73
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Majestic Books, Hounslow, Regno Unito
EUR 152,93
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 149,32
Quantità: 10 disponibili
Aggiungi al carrelloCondizione: New.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 147,82
Quantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 178,19
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Da: moluna, Greven, Germania
EUR 141,46
Quantità: Più di 20 disponibili
Aggiungi al carrelloGebunden. Condizione: New. Mikis D. Stasinopoulos, Robert A. Rigby, Gillian Z. Heller, Vlasios Voudouris, Fernanda De BastianiThis book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Gener.
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 130,30
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 572 pp. Englisch.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 146,84
Quantità: 2 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.