| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w28548 |
| 来源ID | Working Paper 28548 |
| Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression | |
| Bryan S. Graham; Fengshi Niu; James L. Powell | |
| 发表日期 | 2021-03-15 |
| 出版年 | 2021 |
| 语种 | 英语 |
| 摘要 | We study nonparametric regression in a setting where N(N-1) dyadic outcomes are observed for N randomly sampled units. Outcomes across dyads sharing a unit in common may be dependent (i.e., our dataset exhibits dyadic dependence). We present two sets of results. First, we calculate lower bounds on the minimax risk for estimating the regression function at (i) a point and (ii) under the infinity norm. Second, we calculate (i) pointwise and (ii) uniform convergence rates for the dyadic analog of the familiar Nadaraya-Watson (NW) kernel regression estimator. We show that the NW kernel regression estimator achieves the optimal rates suggested by our risk bounds when an appropriate bandwidth sequence is chosen. This optimal rate differs from the one available under iid data: the effective sample size is smaller and dimension of the regressor vector influences the rate differently. |
| 主题 | Econometrics ; Estimation Methods |
| URL | https://www.nber.org/papers/w28548 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/586220 |
| 推荐引用方式 GB/T 7714 | Bryan S. Graham,Fengshi Niu,James L. Powell. Minimax Risk and Uniform Convergence Rates for Nonparametric Dyadic Regression. 2021. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w28548.pdf（600KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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