非线性估计方程的折刀

IF 0.7 Q3 STATISTICS & PROBABILITY Modern Stochastics-Theory and Applications Pub Date : 2022-01-01 DOI:10.15559/22-vmsta208
R. Maiboroda, V. Miroshnychenko, O. Sugakova
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引用次数: 0

摘要

在变浓度混合模型(MVC)中,我们处理的是由属于固定数量的不同总体(混合成分)的受试者组成的非均匀样本。一个对象所属的总体是未知的,但属于给定组成部分的概率是已知的,并且在每次观测中都是不同的。被试观察特征的分布取决于其所属的分量。研究了MVC模型中欧几里得参数的广义估计方程。在适当的假设下,得到的估计量是渐近正态的。描述了一种用于估计其渐近协方差矩阵的叠刀(JK)技术。证明了jk估计量的一致性。给出了一个混合非线性回归模型的应用,并给出了一个实际实例。
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Jackknife for nonlinear estimating equations
In mixture with varying concentrations model (MVC) one deals with a nonhomogeneous sample which consists of subjects belonging to a fixed number of different populations (mixture components). The population which a subject belongs to is unknown, but the probabilities to belong to a given component are known and vary from observation to observation. The distribution of subjects’ observed features depends on the component which it belongs to. Generalized estimating equations (GEE) for Euclidean parameters in MVC models are considered. Under suitable assumptions the obtained estimators are asymptotically normal. A jackknife (JK) technique for the estimation of their asymptotic covariance matrices is described. Consistency of JK-estimators is demonstrated. An application to a model of mixture of nonlinear regressions and a real life example are presented.
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来源期刊
Modern Stochastics-Theory and Applications
Modern Stochastics-Theory and Applications STATISTICS & PROBABILITY-
CiteScore
1.30
自引率
50.00%
发文量
0
审稿时长
10 weeks
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