The place of probability distributions in statistical learning. A commented book review of ‘Distributions for modeling location, scale, and shape using GAMLSS in R’ by Rigby et al. (2021)
Fernando Marmolejo-Ramos, Raydonal Ospina, Freddy Hernández-Barajas
{"title":"The place of probability distributions in statistical learning. A commented book review of ‘Distributions for modeling location, scale, and shape using GAMLSS in R’ by Rigby et al. (2021)","authors":"Fernando Marmolejo-Ramos, Raydonal Ospina, Freddy Hernández-Barajas","doi":"10.1111/anzs.12374","DOIUrl":null,"url":null,"abstract":"<p>Generalised additive models for location, scale and shape (GAMLSS) is a type of distributional regression framework that enables modelling numeric dependent variables via probability distributions other than those of the exponential family. While the cogs behind GAMLSS are provided in Stasinopoulos <i>et al</i>. 2017's book ‘Flexible regression and smoothing using GAMLSS in R, the new book by Rigby <i>et al</i>. considers the distributions implemented in the R software that are usable for GAMLSS modelling. A commented summary of that second book is provided in a supplementary file. Unlike traditional book reviews, two topics in this new book are briefly elaborated on: robustness (Chapter 12) and shape (Chapters 14–16). It is concluded that despite GAMLSS being a powerful and flexible framework for supervised statistical learning, striving for interpretable GAMLSS models is essential.</p>","PeriodicalId":55428,"journal":{"name":"Australian & New Zealand Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australian & New Zealand Journal of Statistics","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/anzs.12374","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
Generalised additive models for location, scale and shape (GAMLSS) is a type of distributional regression framework that enables modelling numeric dependent variables via probability distributions other than those of the exponential family. While the cogs behind GAMLSS are provided in Stasinopoulos et al. 2017's book ‘Flexible regression and smoothing using GAMLSS in R, the new book by Rigby et al. considers the distributions implemented in the R software that are usable for GAMLSS modelling. A commented summary of that second book is provided in a supplementary file. Unlike traditional book reviews, two topics in this new book are briefly elaborated on: robustness (Chapter 12) and shape (Chapters 14–16). It is concluded that despite GAMLSS being a powerful and flexible framework for supervised statistical learning, striving for interpretable GAMLSS models is essential.
期刊介绍：
The Australian & New Zealand Journal of Statistics is an international journal managed jointly by the Statistical Society of Australia and the New Zealand Statistical Association. Its purpose is to report significant and novel contributions in statistics, ranging across articles on statistical theory, methodology, applications and computing. The journal has a particular focus on statistical techniques that can be readily applied to real-world problems, and on application papers with an Australasian emphasis. Outstanding articles submitted to the journal may be selected as Discussion Papers, to be read at a meeting of either the Statistical Society of Australia or the New Zealand Statistical Association.
The main body of the journal is divided into three sections.
The Theory and Methods Section publishes papers containing original contributions to the theory and methodology of statistics, econometrics and probability, and seeks papers motivated by a real problem and which demonstrate the proposed theory or methodology in that situation. There is a strong preference for papers motivated by, and illustrated with, real data.
The Applications Section publishes papers demonstrating applications of statistical techniques to problems faced by users of statistics in the sciences, government and industry. A particular focus is the application of newly developed statistical methodology to real data and the demonstration of better use of established statistical methodology in an area of application. It seeks to aid teachers of statistics by placing statistical methods in context.
The Statistical Computing Section publishes papers containing new algorithms, code snippets, or software descriptions (for open source software only) which enhance the field through the application of computing. Preference is given to papers featuring publically available code and/or data, and to those motivated by statistical methods for practical problems.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
