MCAP is a new multilevel method for regressing covariance matrices on covariates that models cluster-specific projections on the unit sphere with a von Mises-Fisher distribution and estimates parameters via hierarchical likelihood maximization.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2verdicts
UNVERDICTED 2representative citing papers
Extends randomized interventional direct and indirect effects to right-censored semicompeting risks, provides identification and nonparametric estimation, and applies to a hematopoietic cell transplantation study comparing donor types.
citing papers explorer
-
Multilevel Regression Modeling of Covariance Matrix Outcomes
MCAP is a new multilevel method for regressing covariance matrices on covariates that models cluster-specific projections on the unit sphere with a von Mises-Fisher distribution and estimates parameters via hierarchical likelihood maximization.
-
Randomized interventional effects in semicompeting risks, with application to a hematopoietic cell transplantation study
Extends randomized interventional direct and indirect effects to right-censored semicompeting risks, provides identification and nonparametric estimation, and applies to a hematopoietic cell transplantation study comparing donor types.