Adaptive estimation of mixed discrete-continuous distributions under smoothness and sparsity

经济学家经常分析这样的问题，比如消费者是否会买车(是或不会)，如果买了车，花多少钱买车(这是一个可以接受任何价值的金额)。经济学家没有一个好的方法来分析这些问题。本研究项目将开发一种新的、有效的方法来分析具有这些特征的数据。这一结果将为社会科学研究人员分析涉及复杂数据结构的问题提供重要工具。随着日益复杂的数据收集和强大的计算能力的可用，一种能够有效地分析如此复杂的数据集的方法将对各个领域的研究人员和政策制定者具有极其重要的价值。这项研究中开发的方法将被编程，所有研究人员都可以免费使用。这项研究的结果将使研究人员能够向政策制定者提供更准确的建议，从而改善决策和经济增长。这项研究项目开发了一个估计混合离散-连续分布的框架，其中分布的多变量离散部分可以具有大量或少量的支撑点；可以是光滑的，也可以不是光滑的，并且这些特征可以从一个离散坐标到另一个离散坐标不同。在这些设置下，将得到离散-连续分布估计的最优收敛速度。初步结果表明，平滑只对具有快速增长的支撑点数量或足够高的平滑度的离散变量子集有利。所提出的估计方法是基于具有协变量相关混合权重的多变量正态分布的贝叶斯混合。建议的方法将为标准计量经济学模型提供实用和最佳的自适应非参数替代方案，如有序Probit和泊松回归，以及结构性离散选择模型两阶段估计程序中的第一阶段估计器。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Economists often analyze questions such as whether a consumer will buy a car or not (yes or no) and if so how much to spend on the car (an amount that takes any value). Economists do not have a good method to analyze these questions. This research project will develop a new and efficient method to analyze data that has these characteristics. The results will give social science researchers an important tool to analyze problems that involve complicated data structures. With ever increasing sophisticated data collection and the availability of powerful computing power, a method that can be used to efficiently analyze such complicated data sets will be extremely valuable to researchers and policy makers in all fields. The methods developed in this research will be programmed and be freely available to all researchers. The results of this research will allow researchers to give more accurate advice to policy makers and thus improve decision making and economic growth. This research project develops a framework for estimating mixed discrete-continuous distributions where the multivariate discrete part of the distribution can have either a large or a small number of support points; may be smooth or not, and these characteristics can differ from one discrete coordinate to another. The optimal convergence rates for estimation of discrete-continuous distributions will be derived in these settings. Preliminary results suggest that smoothing is beneficial only for a subset of discrete variables with a quickly growing number of support points or sufficiently high level of smoothness. The proposed estimation procedures are based on Bayesian mixtures of multivariate normal distributions with covariate dependent mixing weights. The proposed methods will deliver practical and optimal adaptive nonparametric alternatives to standard econometric models such as ordered probit and Poisson regression, and first stage estimators in two stage estimation procedures for structural discrete choice models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.