概率分布在统计学习中的地位。Rigby等人对《在R中使用GAMLSS建模位置、规模和形状的分布》的书评(2021年)。

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Australian & New Zealand Journal of Statistics Pub Date : 2022-09-23 DOI:10.1111/anzs.12374

Fernando Marmolejo-Ramos, Raydonal Ospina, Freddy Hernández-Barajas

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引用次数: 0

摘要

广义加性位置、规模和形状模型(GAMLSS)是一种分布回归框架，它可以通过概率分布而不是指数族分布来模拟数值因变量。尽管Stasinopoulos等人在2017年出版的《在R中使用GAMLSS进行灵活回归和平滑》一书中提供了GAMLSS背后的细节，但Rigby等人的新书考虑了R软件中实现的可用于GAMLSS建模的分布。在补充文件中提供了第二本书的评论摘要。与传统的书评不同，这本新书中简要阐述了两个主题:稳健性(第12章)和形状(第14-16章)。结论是，尽管GAMLSS是一个强大而灵活的监督统计学习框架，但努力建立可解释的GAMLSS模型是必不可少的。

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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)

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.

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来源期刊

Australian & New Zealand Journal of Statistics 数学-统计学与概率论

CiteScore

1.30

自引率

9.10%

发文量

审稿时长

>12 weeks

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.