Bias correction of diagnostics data from IoT devices
Selection bias in non-probability samples presents a significant challenge in IoT device di- agnostics data analysis, where incomplete or systematically missing data can compromise the validity of analytical insights. Our project proposes a comprehensive statistical frame- work to correct for that bias, using real-world data from Axis Communications AB. The project explores three primary methodolo