UID:
almafu_9961417111902883
Umfang:
1 online resource (xx, 285 pages) :
,
digital, PDF file(s).
Ausgabe:
First edition.
ISBN:
9781009410052
,
1009410059
,
9781009410076
,
1009410075
Serie:
Cambridge series in statistical and probabilistic mathematics ; 56
Inhalt:
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.
Anmerkung:
Title from publisher's bibliographic system (viewed on 22 Feb 2024).
,
Distributional regression models -- Distributions -- Additive model terms -- Inferential methods -- Penalized maximum likelihood inference -- Bayesian inference -- Statistical boosting for GAMLSS -- Fetal ultrasound -- Speech intelligibility testing -- Social media post performance -- Childhood undernutrition in India -- Socioeconomic determinants of federal election outcomes in Germany -- Variable selection for gene expression data.
Weitere Ausg.:
ISBN 9781009410069
Weitere Ausg.:
ISBN 1009410067
Sprache:
Englisch
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