most citedMultiple Imputation Guided by Full Law and Target Law Identifiability

1 citations · 1 across the 2 of their papers we have counts for

collaborators

6 papers

math.ST20261 cited

Multiple Imputation Guided by Full Law and Target Law Identifiability

Juha Karvanen, Santtu Tikka

The central challenges in missing data models concern the identifiability of two distributions: the target law and the full law. The target law refers to the joint distribution of…

stat.ML2026

Clustering and Pruning in Causal Data Fusion

Otto Tabell, Santtu Tikka, Juha Karvanen

Data fusion, the process of combining observational and experimental data, can enable the identification of causal effects that would otherwise remain non-identifiable. Although id…

stat.ME2026

dynamite: An R Package for Dynamic Multivariate Panel Models

Santtu Tikka, Jouni Helske

dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package suppor…

cs.CY2026

Early Warning Signals Appear Long Before Dropping Out: An Idiographic Approach Grounded in Complex Dynamic Systems Theory

Mohammed Saqr, Sonsoles López-Pernas, Santtu Tikka +1

The ability to sustain engagement and recover from setbacks (i.e., resilience) -- is fundamental for learning. When resilience weakens, students are at risk of disengagement and ma…

stat.ME2025

Monotone Missing Data: A Blessing and a Curse

Santtu Tikka, Juha Karvanen

Monotone missingness is commonly encountered in practice when a missing measurement compels another measurement to be missing. Because of the simpler missing data pattern, monotone…

cs.SI2025

Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes

Mohammed Saqr, Sonsoles López-Pernas, Tiina Törmänen +3

This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation…