This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox et al. (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing this link, we extend the estimator, transforming it into a special case of the nonnegative elastic net. The extension improves the estimator’s recovery of the true support and allows for more accurate estimates of the random coefficients’ distribution. Our estimator is a generalization of the original estimator and therefore, is guaranteed to have a model fit at least as good as the original one. A theoretical analysis of both estimators’ properties shows that, under conditions, our generalized estimator approximates the true distribution more accurately. Two Monte Carlo experiments and an application to a travel mode data set illustrate the improved performance of the generalized estimator.

本文研究并扩展了 Fox 等人的计算上有吸引力的非参数随机系数估计器。（2011）。我们证明了他们的估计器是非负 LASSO 的一个特例，解释了它在许多应用中观察到的稀疏性质。认识到这个联系，我们扩展了估计器，将其转换为非负弹性网络的一个特例。该扩展改进了估计器对真实支持的恢复，并允许更准确地估计随机系数的分布。我们的估计器是原始估计器的泛化，因此保证模型拟合至少与原始估计器一样好。对这两个估计器属性的理论分析表明，在一定条件下，我们的广义估计器更准确地逼近真实分布。