Inference in a linear functional relationship with replications

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Julio Hokama, P. Morettin, H. Bolfarine, M. Galea
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引用次数: 1

Abstract

In this paper, we consider a model for data analysis with measurement errors. The main objective of this work is to develop statistical inference tools, such as parameter estimation and hypothesis tests in a linear functional relationship with replicated observations. For this purpose, we use the maximum likelihood method in the presence of incidental parameters, and the unbiased estimating equations approach. Both approaches lead to explicit expressions for the asymptotic covariance matrices of the estimators of the model parameters. A simulation study is performed to assess the empirical behavior of estimators and of a Wald statistic. The methodology is illustrated with a real data set.
与复制的线性函数关系中的推论
在本文中,我们考虑了一个具有测量误差的数据分析模型。这项工作的主要目标是开发统计推断工具,如参数估计和假设检验与重复观测的线性函数关系。为此,我们在存在附带参数的情况下使用极大似然方法和无偏估计方程方法。两种方法都可以得到模型参数估计量的渐近协方差矩阵的显式表达式。进行了模拟研究,以评估估计器和Wald统计量的经验行为。用一个实际数据集说明了该方法。
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
CiteScore
1.60
自引率
10.00%
发文量
30
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.
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