表題番号：2019E-089
日付：2020/04/07
研究課題Model Selection of local linear regression with depend disturbances for large datasets
| 研究者所属（当時） | 資格 | 氏名 | |
|---|---|---|---|
| （代表者） | 国際学術院 国際教養学部 | 助手 | セツ ギョクケツ |
- 研究成果概要
- It is believed that the more information obtained, the more suitable decision we can make. However, it is based on the fundament that the ability dealing with large datasets is increased. This research provides an approach to find an approximate relationship between large datasets when the relationship variates with respect to time which can be more much complicated than linear relationship (i.e., highly nonlinear), such that the government and enterprises can have an accurate overview. In this research, the nonparametric method known as local linear regression is used in high dimensional problems with LASSO, which helps us shrinkage the variables and has been widely used as a method of model selection, and the case with depend disturbances is considered. It is shown that, when the dimension of parameter is fixed, as the sample size increases, and bandwidth decreases, under certain conditaions, the bias and variance of the estimation converges to 0. When the dimension of parameter increases as sample size increases, under certain conditions, the probability of right selection goes to 1.