Name: Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications (en Inglés)
Price: 304008.00 COP
Availability: InStock
Author: Stasinopoulos Mikis D.; Kneib Thomas; Klein Nadja; Mayr Andreas; Heller Gillian Z.
ISBN: 9781009410069

portada Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications (en Inglés)

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Stasinopoulos Mikis D.; Kneib Thomas; Klein Nadja; Mayr Andreas; Heller Gillian Z.

Idioma

Inglés

N° páginas

306

Encuadernación

Tapa Dura

ISBN13

9781009410069

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications (en Inglés)

Stasinopoulos Mikis D.; Kneib Thomas; Klein Nadja; Mayr Andreas; Heller Gillian Z. (Autor) · Cambridge University Press · Tapa Dura

Libro Nuevo Importado

Envío: 18 a 24 días háb.

$ 608.016$ 304.008

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 100 unidades

$ 304.008

¡Envío Gratis! Llega entre el 17 Jul y el 29 Jul a Bogota, Cundinamarca. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, With Applications (en Inglés)"

An emerging field in statistics, distributional regression facilitates the modelling of the complete conditional distribution, rather than just the mean. This book introduces generalized additive models for location, scale and shape (GAMLSS) - one of the most important classes of distributional regression. Taking a broad perspective, the authors consider penalized likelihood inference, Bayesian inference, and boosting as potential ways of estimating models and illustrate their usage in complex applications. Written by the international team who developed GAMLSS, the text's focus on practical questions and problems sets it apart. Case studies demonstrate how researchers in statistics and other data-rich disciplines can use the model in their work, exploring examples ranging from fetal ultrasounds to social media performance metrics. The R code and data sets for the case studies are available on the book's companion website, allowing for replication and further study.