Time Varying Markov Process with Partially Observed Aggregate Data; An Application to Coronavirus

A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19

Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment. Our method can detect unobserved cluster centers of the epidemics, and estimate the space-time range of the clusters that are useful to construct a warning system.…

在非参数模型中利用汇总数据部分识别个体层面参数

Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model

本文提出在非参数模型中利用汇总数据部分识别个体层面参数的方法，并通过实证分析展示了条件通过率在不同群体和县之间的差异。

A trial emulation approach for policy evaluations with group-level longitudinal data

To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders, and numerous studies aim to estimate their effects. Many statistical and econometric methods, such as difference-in-differences, leverage repeated measurements and variation in timing to estimate policy effects, including in the COVID-19 context. While these methods are less common in epidemiology, epidemiologic researchers are well accustomed to handling similar complexities in studies of individual-level interventions. "Target trial e…

Statistical Inference on Partially Linear Panel Model under Unobserved Linearity

A new statistical procedure, based on a modified spline basis, is proposed to identify the linear components in the panel data model with fixed effects. Under some mild assumptions, the proposed procedure is shown to consistently estimate the underlying regression function, correctly select the linear components, and effectively conduct the statistical inference. When compared to existing methods for detection of linearity in the panel model, our approach is demonstrated to be theoretically justified as well as practically convenient. We provide a computational algorithm that implements the pr…

Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing

When collecting geocoded confidential data with the intent to disseminate, agencies often resort to altering the geographies prior to making data publicly available due to data privacy obligations. An alternative to releasing aggregated and/or perturbed data is to release multiply-imputed synthetic data, where sensitive values are replaced with draws from statistical models designed to capture important distributional features in the collected data. One issue that has received relatively little attention, however, is how to handle spatially outlying observations in the collected data, as commo…

基于汇总数据的条件均值识别与半参数估计

Identification and Semiparametric Estimation of Conditional Means from Aggregate Data

本文提出一种新的方法，用于在仅观察到汇总单位（如地理区域）的平均结果和组指标时，估计组内结果变量的均值。通过弱化识别条件，提出基于部分线性形式的去偏机器学习估计器，并提供半参数敏感性分析和非参数检验，最终推导出有效的置信区间。