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