32 research outputs found
Linear latent variable models: the lava-package
An R package for specifying and estimating linear latent variable models is
presented. The philosophy of the implementation is to separate the model
specification from the actual data, which leads to a dynamic and easy way of
modeling complex hierarchical structures. Several advanced features are
implemented including robust standard errors for clustered correlated data,
multigroup analyses, non-linear parameter constraints, inference with
incomplete data, maximum likelihood estimation with censored and binary
observations, and instrumental variable estimators. In addition an extensive
simulation interface covering a broad range of non-linear generalized
structural equation models is described. The model and software are
demonstrated in data of measurements of the serotonin transporter in the human
brain
Estimating speed-through-water by Dynamic Factor Models fusing metocean and propeller data
In vessel performance analysis, reliable information about speed-through-water (STW) is key for realistic modeling of the single ship’s fuel efficiency. It is paramount that STW measurements are reliable such that they can be adopted as input in fuel consumption forecast models. This paper presents a study where three variations of a Dynamic Factor Model are used to estimate the STW. A simulation study is presented to demonstrate the estimation and smoothing techniques for the Dynamic Factor Model. The model variations are then applied to real vessel data, including STW and propeller measurements, in combination with metocean data (sea currents). The present study suggests that the developed model can minimize the systematic STW measurement error even under highly non-stationary conditions