The one day conference is organized jointly by the Info-Metrics Institute, American University and the Department of Economics of University of California, Riverside.
Interest in nonparametric estimation and inference goes back half a century but has rapidly increased recently (especially with recent advances in computing power) with many new directions of research that cover a vast range of applications in different disciplines. Ongoing research on information-theoretic estimation and inference methods is similarly inter-disciplinary, involving information theory, engineering, mathematical statistics, econometrics and the natural sciences.
This one day conference will explore recent advances in the area of nonparametric estimation and inference and in info-metrics, which may help current and future research combining nonparametric procedures with information-theoretic methods.
The conference organizers encourage submissions of papers on any topic within this overall theme with a particular emphasis on the list below.
Nonparametric procedures and info-metrics methods
Nonparametric and Information-Theoretic Estimation and Inference Methods
Density and Regression Estimation (Nonparametric and Information Methods)
Kernel estimation and info-metrics
Mixed data estimation
Nonparametric and Information time series analysis
Nonparametric and Information based panel data models
Amos Golan (Info-Metrics, American U) - Co-Chair Aman Ullah (UC Riverside and Info-Metrics) - Co-Chair Robin Lumsdaine (Info-Metrics, American U) - Co-Chair Tae Hwy Lee (UC Riverside) - Co-Chair Jeff Racine (McMaster University) Nick Kiefer (Cornell)