000 01930aam a2200229 4500
008 250115b2024 |||||||| |||| 00| 0 eng d
020 _a9781009410069
_c£ 54.99
082 _a519.282
_bS8G3
100 1 _aStasinopoulos, Mikis D.
_9348394
245 1 _aGeneralized additive models for location, scale and shape: a distributional regression approach, with applications
260 _bCambridge University Press
_c2024
_aCambridge
300 _axx, 285p.
_bIncludes references and index
440 _aCambridge series in statistical and probabilistic mathematics
_n56
_9428416
520 _aAn 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.
650 _aRegression analysis
_xMathematical models
_9428417
650 _aTheory of distributions (functional analysis)
_9428418
700 _aKneib, Thomas
_9318987
_eCo-author
700 _aKlein, Nadja
_9428419
_eCo-author
700 _aMayr, Andreas
_9428420
_eCo-author
700 _aHeller, Gillian Z.
_9428421
_eCo-author
942 _2ddc
_cBK
999 _c222757
_d222757