69,043 research outputs found
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
The R package lcmm provides a series of functions to estimate statistical
models based on linear mixed model theory. It includes the estimation of mixed
models and latent class mixed models for Gaussian longitudinal outcomes (hlme),
curvilinear and ordinal univariate longitudinal outcomes (lcmm) and curvilinear
multivariate outcomes (multlcmm), as well as joint latent class mixed models
(Jointlcmm) for a (Gaussian or curvilinear) longitudinal outcome and a
time-to-event that can be possibly left-truncated right-censored and defined in
a competing setting. Maximum likelihood esimators are obtained using a modified
Marquardt algorithm with strict convergence criteria based on the parameters
and likelihood stability, and on the negativity of the second derivatives. The
package also provides various post-fit functions including goodness-of-fit
analyses, classification, plots, predicted trajectories, individual dynamic
prediction of the event and predictive accuracy assessment. This paper
constitutes a companion paper to the package by introducing each family of
models, the estimation technique, some implementation details and giving
examples through a dataset on cognitive aging
Co-evolution of Selection and Influence in Social Networks
Many networks are complex dynamical systems, where both attributes of nodes
and topology of the network (link structure) can change with time. We propose a
model of co-evolving networks where both node at- tributes and network
structure evolve under mutual influence. Specifically, we consider a mixed
membership stochastic blockmodel, where the probability of observing a link
between two nodes depends on their current membership vectors, while those
membership vectors themselves evolve in the presence of a link between the
nodes. Thus, the network is shaped by the interaction of stochastic processes
describing the nodes, while the processes themselves are influenced by the
changing network structure. We derive an efficient variational inference
procedure for our model, and validate the model on both synthetic and
real-world data.Comment: In Proc. of the Twenty-Fifth Conference on Artificial Intelligence
(AAAI-11
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