Model-assisted estimation in high-dimensional settings for survey data

Smoothed Model-Assisted Small Area Estimation

In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore survey design, while traditional small area estimation approaches may not incorporate both unit level covariate information and spatial smoothing in a design-consistent way. We propose a smoothed model-assisted estimator that accounts for survey design and leverages both unit level covariates and…

脑功能连接性的协变量辅助主回归的贝叶斯估计

Bayesian estimation of covariate assisted principal regression for brain functional connectivity

本文提出CAP回归的贝叶斯重新表述，通过将协方差矩阵映射至切空间利用欧几里得几何进行后验推断，在统一框架内实现参数联合估计与不确定性量化，并应用于脑功能连接性分析。

样本调查中非响应情况下的准模型辅助估计器

Quasi Model-Assisted Estimators under Nonresponse in Sample Surveys

本文提出了一类准模型辅助估计器，用于处理样本调查中的非响应问题，通过结合工作模型和响应模型提高估计效率和准确性。

通过整合人口代表性调查数据提高回归分析效率：一种模型辅助校准方法

Improving Efficiency of Regression Analyses by Integrating Data from Population-Representative Surveys: A Model-Assisted Calibration Approach

提出模型辅助校准方法，整合多个概率调查样本以提高回归效率，支持个体或汇总数据，无需正确指定结果模型，并保持有限总体推断的有效性。

Model-assisted estimation through random forests in finite population sampling

In surveys, the interest lies in estimating finite population parameters such as population totals and means. In most surveys, some auxiliary information is available at the estimation stage. This information may be incorporated in the estimation procedures to increase their precision. In this article, we use random forests to estimate the functional relationship between the survey variable and the auxiliary variables. In recent years, random forests have become attractive as National Statistical Offices have now access to a variety of data sources, potentially exhibiting a large number of obs…

当调查变成对话：面向AI辅助访谈的自适应矩阵验证

When Surveys Become Conversations: Adaptive Matrix Validation for AI-Assisted Interviews

提出自适应矩阵验证（AMV）方法，通过少量随机结构化问题校准AI从访谈中映射的变量，提升均值、子组和回归系数的估计精度，并在三个模拟研究中验证其有效性。

Automated Selection of Post-Strata using a Model-Assisted Regression Tree Estimator

Auxiliary information can increase the efficiency of survey estimators through an assisting model when the model captures some of the relationship between the auxiliary data and the study variables. Despite their superior properties, model-assisted estimators are rarely used in anything but their simplest form by statistical agencies to produce official statistics. This is due to the fact that the more complicated models that have been used in model-assisted estimation are often ill suited to the available auxiliary data. Under a model-assisted framework, we propose a regression tree estimator…

基于贝叶斯优化的模型辅助调查抽样

Model-assisted survey sampling with Bayesian optimization

针对固定预算下调查抽样的样本优化分配需求，提出一种结合贝叶斯优化的模型辅助无偏概率抽样方法，经芬兰中部树木蓄积量数据集模拟验证，其估计量方差效率显著优于传统基准方法。

使用模型辅助校准方法提高复杂调查设计下两相样本回归分析的效率

Using Model-Assisted Calibration Methods to Improve Efficiency of Regression Analyses with Two-Phase Samples under Complex Survey Designs

本文提出一种基于回归系数得分函数的模型辅助校准方法，通过将两相子样本权重校准至加权的整个第一相样本，有效提高了复杂调查设计下两相样本回归分析的效率。

Model-assisted sensitivity analysis for treatment effects under unmeasured confounding via regularized calibrated estimation

Consider sensitivity analysis for estimating average treatment effects under unmeasured confounding, assumed to satisfy a marginal sensitivity model. At the population level, we provide new representations for the sharp population bounds and doubly robust estimating functions, recently derived by Dorn, Guo, and Kallus. We also derive new, relaxed population bounds, depending on weighted linear outcome quantile regression. At the sample level, we develop new methods and theory for obtaining not only doubly robust point estimators for the relaxed population bounds with respect to misspecificatio…

Network-Assisted Estimation for Large-dimensional Factor Model with Guaranteed Convergence Rate Improvement

Network structure is growing popular for capturing the intrinsic relationship between large-scale variables. In the paper we propose to improve the estimation accuracy for large-dimensional factor model when a network structure between individuals is observed. To fully excavate the prior network information, we construct two different penalties to regularize the factor loadings and shrink the idiosyncratic errors. Closed-form solutions are provided for the penalized optimization problems. Theoretical results demonstrate that the modified estimators achieve faster convergence rates and lower as…

The correlation-assisted missing data estimator

We introduce a novel approach to estimation problems in settings with missing data. Our proposal -- the Correlation-Assisted Missing data (CAM) estimator -- works by exploiting the relationship between the observations with missing features and those without missing features in order to obtain improved prediction accuracy. In particular, our theoretical results elucidate general conditions under which the proposed CAM estimator has lower mean squared error than the widely used complete-case approach in a range of estimation problems. We showcase in detail how the CAM estimator can be applied t…