数据回归模型中的L'P误差和混合序列问题综述

This paper focuses on two main parts. In the first part, the problem of the nth-mean, complete consistency for the estimators of a nonparametric and linear model with L' mixingale errors is discussed. The second part addresses the issue of the nth-mean, complete consistency for the estimators of the models with weak stationary linear process errors and the uniformly mean consistency. In the nonparametric model Y, = we explore the concept of L' mixingale errors, which are a type of random variable sequence that satisfy certain mixing properties. These errors can introduce challenges in estimating the parameters of the model, particularly in terms of achieving consistency and reducing bias. Furthermore, the discussion also includes the concept of weak stationary linear process errors, which are a type of stochastic process characterized by a constant mean and covariance that is independent of time. The errors in this type of model can also pose challenges for estimation and require special consideration to ensure the accuracy and reliability of the results. Overall, this paper delves into the complexities of regression models with different types of errors and explores strategies to achieve reliable and consistent estimation in the presence of these challenges. The insights provided in this study can be valuable for researchers and practitioners in the field of statistical modeling and data analysis.