Combinatorial Mixtures of Multiparameter Distributions: An Application to Bivariate Data

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2017-02-16 DOI:10.1515/ijb-2015-0064

V. Edefonti, G. Parmigiani

引用次数: 0

Abstract

Abstract: We introduce combinatorial mixtures – a flexible class of models for inference on mixture distributions whose components have multidimensional parameters. The key idea is to allow each element of the component-specific parameter vectors to be shared by a subset of other components. This approach allows for mixtures that range from very flexible to very parsimonious and unifies inference on component-specific parameters with inference on the number of components. We develop Bayesian inference and computational approaches for this class of distributions, and illustrate them in an application. This work was originally motivated by the analysis of cancer subtypes: in terms of biological measures of interest, subtypes may be characterized by differences in location, scale, correlations or any of the combinations. We illustrate our approach using publicly available data on molecular subtypes of lung and prostate cancers.

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多参数分布的组合混合：在双变量数据中的应用

摘要：我们介绍了组合混合物——一类灵活的模型，用于推断混合物分布，其成分具有多维参数。关键思想是允许特定于组件的参数向量的每个元素由其他组件的子集共享。这种方法允许从非常灵活到非常简约的混合，并将对组件特定参数的推断与对组件数量的推断统一起来。我们为这类分布开发了贝叶斯推理和计算方法，并在应用中进行了说明。这项工作最初的动机是分析癌症亚型：就感兴趣的生物学测量而言，亚型可能以位置、规模、相关性或任何组合的差异为特征。我们使用公开的肺癌和前列腺癌分子亚型的数据来说明我们的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.