Parameter estimation and hypothesis tests in logistic model for complex correlated data

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Statistics & Probability Letters Pub Date : 2024-11-02 DOI:10.1016/j.spl.2024.110294

Keyi Mou, Zhiming Li, Jinlong Cheng

引用次数: 0

Abstract

Observations are frequently generated in clinical trials from correlated multiple organs (or parts) of individuals. The statistical inference is little about conducting regression analysis based on such data. This paper first develops a logistic regression for correlated multiple responses using a stable correlation binomial (SCB) model. Then, we obtain maximum likelihood estimators (MLEs) of unknown parameters through a fast quadratic lower bound (QLB) algorithm. Further, likelihood ratio, score and Wald statistics are used to test the effect of covariates based on the MLEs. Finally, the QLB algorithm and asymptotic tests are evaluated through simulations and applied to real dental data.

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复杂相关数据逻辑模型的参数估计和假设检验

在临床试验中，经常会从相关的多个器官（或部位）中观察到个体的情况。基于此类数据进行回归分析的统计推断很少。本文首先利用稳定相关二项（SCB）模型开发了相关多重反应的逻辑回归。然后，我们通过快速二次下界（QLB）算法获得未知参数的最大似然估计值（MLE）。然后，根据 MLEs 使用似然比、得分和 Wald 统计量来检验协变量的影响。最后，通过模拟对 QLB 算法和渐近检验进行评估，并将其应用于真实的牙科数据。

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

Statistics & Probability Letters 数学-统计学与概率论

CiteScore

1.60

自引率

0.00%

发文量

173

审稿时长

6 months

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