Andreas Mayr, Gillian Z. Heller, Mikis D. Stasinopoulos, Nadja Klein, Thomas Kneib Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications 9781009410069

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Автор: Andreas Mayr, Gillian Z. Heller, Mikis D. Stasinopoulos, Nadja Klein, Thomas Kneib
Название: Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications
ISBN: 9781009410069
Издательство: Cambridge Academ
Классификация:

Анализ данных: Общие положения

Эконометрика

Экономическая статистика

Вероятность и статистика

ISBN-10: 1009410067
Обложка/Формат: Hardback
Страницы: 299
Вес: 0.76 кг.
Дата издания: 29.02.2024
Серия: Cambridge series in statistical and probabilistic mathematics
Язык: English
Иллюстрации: Worked examples or exercises
Размер: 186 x 264 x 28
Ключевые слова: MATHEMATICS / Probability & Statistics / General
Подзаголовок: A distributional regression approach, with applications
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: 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 texts 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 books companion website, allowing for replication and further study.

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications, Andreas Mayr, Gillian Z. Heller, Mikis D. Stasinopoulos, Nadja Klein, Thomas Kneib