最小二乘估计器的特征：带有因果误差的多变量同调回归模型的错误定义

IF 0.4 Q4 STATISTICS & PROBABILITY

Theory of Probability and Mathematical Statistics Pub Date : 2024-05-10 DOI:10.1090/tpms/1210

Pramita Bagchi, Subhra Dhar

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引用次数: 0

摘要

本文研究了多元等调回归函数（记为 LSEMIR）的最小二乘估计器在模型被错误指定、误差为 β \beta 混合静态随机变量时的一些良好性质。在温和的条件下，可以观察到最小二乘估计器均匀地收敛于某个单调函数，在适当的意义上最接近原始函数。

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Characterization of the least squares estimator: Mis-specified multivariate isotonic regression model with dependent errors

This article investigates some nice properties of the least squares estimator of multivariate isotonic regression function (denoted as LSEMIR), when the model is mis-specified, and the errors are β \beta -mixing stationary random variables. Under mild conditions, it is observed that the least squares estimator converges uniformly to a certain monotone function, which is closest to the original function in an appropriate sense.

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来源期刊

Theory of Probability and Mathematical Statistics STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

0.00%

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