A numerical method to obtain exact confidence intervals for likelihood-based parameter estimators

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
Minsoo Jeong
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引用次数: 1

Abstract

We propose a numerical method for obtaining exact confidence intervals of likelihood-based parameter estimators for general multi-parameter models. Although the test inversion method provides exact confidence intervals, it is applicable only to single-parameter models. Our new method can be applied to general multi-parameter models without loss of accuracy, which is in sharp contrast to other multi-parameter extensions of the test inversion. Using Monte Carlo simulations, we show that our method is feasible and provides correct coverage probabilities in finite samples.

基于似然参数估计的精确置信区间的数值方法
针对一般多参数模型,提出了一种基于似然参数估计的精确置信区间的数值计算方法。虽然测试反演方法提供了精确的置信区间,但它只适用于单参数模型。我们的新方法可以应用于一般的多参数模型而不损失精度,这与其他测试反演的多参数扩展形成鲜明对比。通过蒙特卡罗模拟,我们证明了我们的方法是可行的,并且在有限的样本中提供了正确的覆盖概率。
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来源期刊
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference 数学-统计学与概率论
CiteScore
2.10
自引率
11.10%
发文量
78
审稿时长
3-6 weeks
期刊介绍: The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists. We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
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