贝叶斯广义加法模型选择,包括快速变异选项

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY
Virginia X. He, Matt P. Wand
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引用次数: 0

摘要

我们使用贝叶斯模型选择范式,如组最小绝对收缩和选择算子先验,来促进广义加法模型选择。我们的方法允许将连续预测因子的影响分为零、线性或非线性。采用精心定制的辅助变量,可产生吉布斯马尔科夫链蒙特卡洛方案,用于该方法的实际应用。此外,还获得了具有闭式更新的均值场变分算法。这种快速变异方案虽然精确度不高,但增强了对超大数据集的可扩展性。R 语言的软件包有助于实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Bayesian generalized additive model selection including a fast variational option

Bayesian generalized additive model selection including a fast variational option

Bayesian generalized additive model selection including a fast variational option

We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach allows for the effects of continuous predictors to be categorized as either zero, linear or non-linear. Employment of carefully tailored auxiliary variables results in Gibbsian Markov chain Monte Carlo schemes for practical implementation of the approach. In addition, mean field variational algorithms with closed form updates are obtained. Whilst not as accurate, this fast variational option enhances scalability to very large data sets. A package in the R language aids use in practice.

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来源期刊
Asta-Advances in Statistical Analysis
Asta-Advances in Statistical Analysis 数学-统计学与概率论
CiteScore
2.20
自引率
14.30%
发文量
39
审稿时长
>12 weeks
期刊介绍: AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.
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