M. Prates

Securing Sensitive Data: A Synthetic Data Framework for Protecting Geographic Coordinates

High‐resolution spatial data are critical for environmental and epidemiological research yet present significant privacy risks when released at the individual level. Traditional protection methods, such as aggregation or masking, often compromise data utility or introduce unrealistic spatial patterns. We propose a ro...

A Monte Carlo study and data analysis for time series of counts

Assessment of global and local influence in robust linear and nonlinear censored regression models