PsyArXiv
metadata only
From Probabilistic to Deterministic Missingness: When Predictive Mean Matching Breaks Down Under MAR
Predictive mean matching (PMM) is usually considered a good alround imputation method that yields unbiased statistical inferences in many scenarios. A known drawback of PMM is that it imputes observed values and thus, by design, cannot make extrapolations beyond the range of the observed data. Nor c
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- From Probabilistic to Deterministic Missingness: When Predictive Mean Matching Breaks Down Under MAR#1135273
- Estimação de dados faltantes em séries temporais de irradiação solar: uma abordagem baseada em aprendizado de máquina#1148548
- Nonparametric Imputation of Missing Values for Estimating Equation Based Inference#168025
- Evaluating the Cyclical Hybrid Imputation Technique (CHIT) under MCAR, MAR, and MNAR missing data mechanisms: Evidence from health datasets.#1147058
- Randomization Inference for Matched Pairs with Binary Outcomes#925584
Record · ID 1135423
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive