变量贝塔回归中的乘法误差

IF 0.6 4区数学 Q4 STATISTICS & PROBABILITY

Brazilian Journal of Probability and Statistics Pub Date : 2023-06-01 DOI:10.1214/22-bjps543

Jalmar M. F. Carrasco, S. Ferrari, R. Arellano-Valle

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引用次数: 0

摘要

本文讨论了具有未直接观察到的协变量的贝塔回归模型；相反，它被一个低估其实际值的代理协变所取代。我们提出了一个针对这种情况量身定制的变量乘法误差模型，并开发了未知参数的校准回归和基于伪似然的推断。通过仿真和实际数据说明，评估了忽略测量误差的影响和推理方法的性能。

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Multiplicative errors-in-variables beta regression

. This paper deals with beta regression models with a covariate that is not directly observed; instead, it is replaced by a surrogate covariate that underpredicts its actual value. We propose a multiplicative errors-in-variables model tailored for this situation and develop calibration regression and pseudo-likelihood-based inference for the unknown parameters. The impact of ignoring the measurement error and the performance of the inference methods are evaluated through simulations and a real data illustration.

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来源期刊

Brazilian Journal of Probability and Statistics STATISTICS & PROBABILITY-

CiteScore

1.60

自引率

10.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Brazilian Journal of Probability and Statistics aims to publish high quality research papers in applied probability, applied statistics, computational statistics, mathematical statistics, probability theory and stochastic processes. More specifically, the following types of contributions will be considered: (i) Original articles dealing with methodological developments, comparison of competing techniques or their computational aspects. (ii) Original articles developing theoretical results. (iii) Articles that contain novel applications of existing methodologies to practical problems. For these papers the focus is in the importance and originality of the applied problem, as well as, applications of the best available methodologies to solve it. (iv) Survey articles containing a thorough coverage of topics of broad interest to probability and statistics. The journal will occasionally publish book reviews, invited papers and essays on the teaching of statistics.