Proposal Description

This Invited Paper Session, organized under the auspices of the IASC, is designed to showcase the work of young researchers engaged in statistical computing and related computational methods. The session brings together early-career statisticians whose research spans innovative methodological developments, computational techniques, and applications across diverse areas of statistics, including machine learning, survival analysis, and spatial statistics.
The three contributions in this session illustrate this diversity. The first presentation explores hybrid approaches that combine classical time-series volatility models with deep learning architectures for financial forecasting. The second addresses methodological refinements to distance-based classification methods for mixed-type data, a common challenge in applied statistical computing. The third applies competing risks and spatial frailty models to time-to-event data in a public health context, illustrating the role of computational statistics in addressing real-world epidemiological questions.
The session aligns with IASC's mission to promote the development and dissemination of statistical computing methods, while specifically supporting young researchers by providing them with a platform for international visibility at the ISI World Statistics Congress. By bringing together young researchers from different countries and institutions, this session also strengthens the global network of early-career statisticians within IASC, contributing to capacity building and the long-term growth of the statistical computing community.