Survey Statistics: design-based cross validation (dCV)

2026-03-31 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Research Methodology & Innovation · Depth: Advanced, quick

Summary

The discussion addresses the challenge of splitting structured data into train and test sets for cross-validation, particularly when dealing with cross-validation noise. It references Aki Vehtari's FAQ on data splitting, which outlines various methods including leave-one-group-out (LOGO) cross-validation. The post also highlights Thomas Lumley's work on using "replicate weights" for cross-validation in complex survey data, as detailed in Iparragirre et al. (2023). A method called design-based cross-validation (dCV) is presented as a promising approach. dCV involves splitting primary sampling units (PSUs) instead of individuals, rejecting splits where an entire stratum falls into one fold, and modifying weights to ensure each subsample replicates the original sample.

Key takeaway

For AI scientists evaluating models on structured or survey data, adopting design-based cross-validation (dCV) is crucial. This method, by splitting primary sampling units and adjusting weights, ensures your model's performance estimates are more robust and reflective of real-world generalization, especially when dealing with complex data hierarchies and sampling designs. You should investigate dCV to avoid misleading cross-validation noise.

Key insights

Design-based cross-validation (dCV) improves model evaluation for structured data by respecting sampling design.

Principles

• Split primary sampling units, not individuals.
• Avoid splits where a stratum is entirely in one fold.
• Modify weights to replicate original sample distribution.

Method

Design-based cross-validation (dCV) applies K-fold CV by splitting Primary Sampling Units (PSUs), rejecting splits that isolate strata, and adjusting subsample weights to match the original sample's characteristics.

In practice

• Apply dCV for survey data with PSUs and strata.
• Consider LOGO CV for grouped data structures.
• Use replicate weights for variance estimation.

Topics

• Design-based Cross Validation
• Replicate Weights
• Primary Sampling Units
• Cross-Validation Noise
• Complex Survey Data

Best for: AI Scientist, Data Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.