﻿ 均值方差联合模型的SEE变量选择 SEE Variable Selection for Joint Mean and Variance Models

Statistics and Application
Vol.06 No.01(2017), Article ID:20020,6 pages
10.12677/SA.2017.61011

SEE Variable Selection for Joint Mean and Variance Models

Ting Yao, Fengting Lu, Ruiqin Tian, Qiaoqiao Lv

Department of Statistics, Zhejiang Agricultural and Forestry University, Hangzhou Zhejiang

Received: Mar. 9th, 2017; accepted: Mar. 26th, 2017; published: Mar. 29th, 2017

ABSTRACT

The method based on modeling the variance is one of the most commonly used methods to deal with heteroscedasticity. In this paper, we propose a variable selection procedure based on the smooth threshold estimating equations for joint mean and variance models. The proposed variable selection method can select variables and estimate coefficients simultaneously, and does not need to solve convex optimization problem so as to largely reduce computation quantity in practice. Finally, we make some simulations to show that the proposed procedure works satisfactorily.

Keywords:Joint Mean and Variance Models, Heteroscedasticity, Estimating Equation, Variable Selection

1. 引言

2. 基于光滑阈估计方程的变量选择

2.1. 均值方差联合模型

(1)

2.2. 光滑阈估计方程

(2)

(3)

(4)

(5)

2.3. 调整参数的选择

(6)

.

3. 迭代计算

(7)

(8)

(9)

(10)

4. 模拟研究

(11)

(12)

3) 在固定的变量选择模型和固定的样本量n下，均值模型的变量选择效果均要优于方差模型的变量选择效果。

5. 结论

Table 1. The results of variable selection for joint mean and variance models based on different methods.

SEE Variable Selection for Joint Mean and Variance Models[J]. 统计学与应用, 2017, 06(01): 98-103. http://dx.doi.org/10.12677/SA.2017.61011

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