The Center for Statistical Research & Methodology focuses on applying and developing new methods in the following eight areas of statistical research.

Missing Data & Observational Data Modeling

Missing data and observational data modeling methods are used to compensate when some or all of the data are not captured for some responding units.

Record Linkage & Machine Learning

Record linkage and machine learning methods are used for matching or linking records among various data sets.

Small Area Estimation

Small area estimation methods provide reliable data products for finer geographic levels and subpopulations when sample sizes for these levels are insufficient.

Spatial Analysis & Modeling

Spatial analysis and modeling methods are used to develop descriptive statistics, build models, and predict outcomes using geographically referenced data.

Sampling Estimation & Survey Inference

Sampling estimation and survey inference methods are used for taking sample data and making valid inferences about populations of people or businesses.

Time Series & Seasonal Adjustment

Seasonal adjustment methods are used to monitor activity over time and to remove a component of a time series that reveals a seasonal effect in the data.

Experimentation, Prediction, & Modeling

Experimentation, prediction, and modeling methods are used to build models and design experiments to answer questions related to testing.

Simulation, Data Science, & Visualization

Simulation and data science methods are used to build models and to carry out computer simulations designed under realistic data collection conditions.

Statistical Expertise & General Research Topics [<1.0 MB]

Page Last Revised - February 27, 2023

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