3 papers
stat.ME2026
GFCM: A Tail-Sensitive Mixed-Type Conditional Independence Test for Causal Discovery
Pavel Averin, Theodoros Moysiadis, Ioannis Katakis
Constraint-based causal discovery like PC and FCI depends on its conditional independence test. Partial correlation and the Generalised Covariance Measure (GCM) detect only the con…
stat.ML2026
Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey
Pavel Averin, Theodoros Moysiadis, Ioannis Katakis
Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleto…
math.ST2015
On Locally Dyadic Stationary Processes
Theodoros Moysiadis, Konstantinos Fokianos
We introduce the concept of local dyadic stationarity, to account for non-stationary time series, within the framework of Walsh-Fourier analysis. We define and study the time varyi…