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

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

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

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

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

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