Identification of proportionality structure with two-part models using penalization

Kuangnan Fang, Xiaoyan Wang, Ben Chang Shia, Shuangge Ma

研究成果: 雜誌貢獻文章同行評審

3 引文 斯高帕斯(Scopus)


Data with a mixture distribution are commonly encountered. A special example is zero-inflated data, where a proportion of the responses takes zero values, and the rest are continuously distributed. Such data routinely arise in public health, biomedicine, and many other fields. Two-part modeling is a natural choice for zero-inflated data, where the first part of the model describes whether the responses are equal to zero, and the second part describes the continuously distributed responses. With two-part models, an interesting problem is to identify the proportionality structure of covariate effects. Such a structure can lead to more efficient estimates and also provide scientific insights into the underlying data-generating mechanisms. To identify the proportionality structure, we adopt a penalization method. Compared to the alternatives, notable advantages of this method include computational simplicity, solid statistical properties, and others. For inference, we adopt a bootstrap approach. The proposed method shows satisfactory performance in simulation and the analysis of two public health datasets.
頁(從 - 到)12-24
期刊Computational Statistics and Data Analysis
出版狀態已發佈 - 7月 1 2016

ASJC Scopus subject areas

  • 計算數學
  • 計算機理論與數學
  • 統計與概率
  • 應用數學


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