Double-smoothing Local Linear Regression Analysis Of Clustered Data

In the analysis of statistical data, we often use the nonparametric regression method. As one of the nonparametric regression methods, the application of local polynomial smoothing is very extensive. In this research, we mainly use the double-smoothing local linear regression method to analyze clustered data. That is we need fit the curve again on the basis of the local linear regression. The following we will give a brief introduction of the specific content of this article.We start from the background of this article, and through the analysis of the research background and research results, we will illustrate the importance of this article research. Because what we mainly study is to use the local linear regression and the double-smoothing local linear regression method to analyze clustered data. And the local linear regression as a special case of local polynomial regression. So we will firstly do a simple introduction to the local polynomial regression. Then we will study the local linear regression method for the clustered data. Then we focuses on how to use the double-smoothing method dealing with the clustered data. Compared with the local linear regression method, the double-smoothing method make full use of the information contained in the estimator. For these methods, we will conclude their asymptotic bias and variance. Then we would compare these asymptotic properties. Finally we found that the double-smoothing method is superior to the local linear regression in dealing with the clustered data. It is consistent with the conclusion about the other data.