Adaptive estimation in the linear random coefficients model when regressors have limited variation
Adaptive estimation in the linear random coefficients model when regressors have limited variation
复制标题
当回归量变化有限时线性随机系数模型中的自适应估计
作者：
C. Gaillac;E. Gautier
我们考虑一个线性模型，其中系数（截距和斜率）是随机的并且独立于支持真子集的回归量。当密度具有有限加权L 2 范数时，对于精心选择的权重，随机系数的联合密度被识别。对于该模型和相关的白噪声模型，导出了密度估计的最高风险的下限。我们提出了一个估计器、它的收敛率以及提供自适应估计器的数据驱动规则。 CRAN.R 上提供了实现我们的估计器的 R 包 RandomCoefficients。
We consider a linear model where the coefficients-intercept and slopes-are random and independent from regressors which support is a proper subset. When the density has finite weighted L 2 norm, for well chosen weights, the joint density of the random coefficients is identified. Lower bounds on the supremum risk for the estimation of the density are derived for this model and a related white noise model. We present an estimator, its rates of convergence, and a data-driven rule which delivers adaptive estimators. An R package RandomCoefficients that implements our estimator is available on CRAN.R.
影响因子：
1.5
作者：
Holzmann;A. Meister
通讯作者：
A. Meister