零膨胀和多源零的测量误差模型,以及对硬零的应用。

IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-05-28 DOI:10.1007/s10985-024-09627-w
Anindya Bhadra, Rubin Wei, Ruth Keogh, Victor Kipnis, Douglas Midthune, Dennis W Buckman, Ya Su, Ananya Roy Chowdhury, Raymond J Carroll
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引用次数: 0

摘要

我们考虑的是重复观测并存在测量误差的两个变量的测量误差模型。其中一个变量是连续的,而另一个变量是连续测量和零测量的混合。第二个变量有两个零点来源。第一个来源是偶发零,即一个人的某些测量值可能为零,而另一些测量值可能为正。第二个来源是硬性零,即有些人总是报告零。例如,酒精饮料的酒精消耗量:有些人偶尔饮用酒精饮料,而有些人则从不饮用酒精饮料。然而,由于重复测量的个体数量较少,因此无法确定哪些是偶发性零,哪些是硬性零。我们针对这一问题建立了一个新的测量误差模型,并使用贝叶斯方法对其进行拟合。模拟和数据分析用于说明我们的方法。我们还简要讨论了对参数模型和生存分析的扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros.

We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

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来源期刊
Lifetime Data Analysis
Lifetime Data Analysis 数学-数学跨学科应用
CiteScore
2.30
自引率
7.70%
发文量
43
审稿时长
3 months
期刊介绍: The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
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