Optimal allocation in stratified cluster‐based outcome‐dependent sampling designs
In public health research, finite resources often require that decisions be made at the study
design stage regarding which individuals to sample for detailed data collection. At the same …
design stage regarding which individuals to sample for detailed data collection. At the same …
Outcome dependent subsampling divide and conquer in generalized linear models for massive data
J Yin, J Ding, C Yang - Journal of Statistical Planning and Inference, 2025 - Elsevier
In order to break the constraints and barriers caused by limited computing power in
processing massive datasets, we propose an outcome dependent subsampling divide and …
processing massive datasets, we propose an outcome dependent subsampling divide and …
Practical strategies for operationalizing optimal allocation in stratified cluster‐based outcome‐dependent sampling designs
Cluster‐based outcome‐dependent sampling (ODS) has the potential to yield efficiency
gains when the outcome of interest is relatively rare, and resource constraints allow only a …
gains when the outcome of interest is relatively rare, and resource constraints allow only a …
Cluster-based outcome-dependent sampling: inference and frameworks for efficient sampling designs
S Sauer - 2021 - dash.harvard.edu
Efficient sampling designs are valuable in public health research when finite resources
necessitate decisions regarding which individuals to sample for detailed data collection. In …
necessitate decisions regarding which individuals to sample for detailed data collection. In …
[CITATION][C] Constrained Maximum Likelihood Estimation Under Two-Phase Designs Using Group-Level Information in Cluster-Correlated Settings
YJ Kim - 2022 - ResearchSpace@ Auckland