arXiv subjects

Ethan Pawl

Publications and source records attributed to Ethan Pawl.

2 recordsLinked to original sources

arXiv2026

Nonprobability Samples for Small Area Estimation: A Review and Comparative Simulation Study

Nonprobability samples (NPS) are attractive because they are less costly to collect, can provide substantially larger sample sizes, and may reach populations that traditional probability surveys do not. As response rates for traditional surveys fall, interest in NPS has grown rapidly within the field of survey statistics. These methods are especially relevant for small area estimation (SAE), where there is ever-present demand for estimates at fine geographic scales and detailed demographic domains. Despite rapid methodological development, there remains limited understanding of which approaches perform best under different conditions. In this paper, we review recent developments in NPS methodology, including the concept of data defect correlation (DDC) as a measure of data quality and as a tool for categorizing the various NPS methods. We then present a comprehensive simulation study that evaluates a range of NPS approaches under varying levels of DDC and extend several existing methods to the SAE setting.

stat.ME↗

arXiv2025

Mixtures of Neural Network Experts with Application to Phytoplankton Flow Cytometry Data

Flow cytometry is a valuable technique that measures the optical properties of particles at a single-cell resolution. When deployed in the ocean, flow cytometry allows oceanographers to study different types of photosynthetic microbes called phytoplankton. It is of great interest to study how phytoplankton properties change in response to environmental conditions. In our work, we develop a nonlinear mixture of experts model to estimate separate regression functions for each subpopulation utilizing random-weight neural networks. Our model allows one to flexibly estimate how cell properties and relative abundances depend on environmental covariates in each segment of a heterogeneous sample, without the computational burden of backpropagation. We show that the proposed model provides superior predictive performance in simulated examples compared to a mixture of linear experts. Also, applying our model to real data, we show that our model has (1) comparable out-of-sample prediction performance, and (2) more realistic estimates of phytoplankton behavior.

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