Uncovering ecological state dynamics with hidden Markov models
Ecological systems can often be characterised by changes among a finite set of underlying
states pertaining to individuals, populations, communities or entire ecosystems through time …
states pertaining to individuals, populations, communities or entire ecosystems through time …
Joint latent class models for longitudinal and time-to-event data: a review
C Proust-Lima, M Séne, JMG Taylor… - … methods in medical …, 2014 - journals.sagepub.com
Most statistical developments in the joint modelling area have focused on the shared
random-effect models that include characteristics of the longitudinal marker as predictors in …
random-effect models that include characteristics of the longitudinal marker as predictors in …
[图书][B] Model-based clustering and classification for data science: with applications in R
Cluster analysis finds groups in data automatically. Most methods have been heuristic and
leave open such central questions as: how many clusters are there? Which method should I …
leave open such central questions as: how many clusters are there? Which method should I …
[图书][B] Principles of system identification: theory and practice
AK Tangirala - 2018 - taylorfrancis.com
Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-
driven or measurement-based process operations, system identification is an interface that …
driven or measurement-based process operations, system identification is an interface that …
Estimation of extended mixed models using latent classes and latent processes: the R package lcmm
C Proust-Lima, V Philipps, B Liquet - arXiv preprint arXiv:1503.00890, 2015 - arxiv.org
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 …
linear mixed model theory. It includes the estimation of mixed models and latent class mixed …
[图书][B] Mixture model-based classification
PD McNicholas - 2016 - taylorfrancis.com
" This is a great overview of the field of model-based clustering and classification by one of
its leading developers. McNicholas provides a resource that I am certain will be used by …
its leading developers. McNicholas provides a resource that I am certain will be used by …
[HTML][HTML] Model-based clustering
PD McNicholas - Journal of Classification, 2016 - Springer
The notion of defining a cluster as a component in a mixture model was put forth by
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
Tiedeman in 1955; since then, the use of mixture models for clustering has grown into an …
[图书][B] Finite mixture and Markov switching models
S Frühwirth-Schnatter - 2006 - Springer
Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …
applications exploding. Finite mixture models are nowadays applied in such diverse areas …
[HTML][HTML] An approach for determining the number of clusters in a model-based cluster analysis
S Akogul, M Erisoglu - Entropy, 2017 - mdpi.com
To determine the number of clusters in the clustering analysis that has a broad range of
applied sciences, such as physics, chemistry, biology, engineering, economics etc., many …
applied sciences, such as physics, chemistry, biology, engineering, economics etc., many …
Local solutions in the estimation of growth mixture models.
Finite mixture models are well known to have poorly behaved likelihood functions featuring
singularities and multiple optima. Growth mixture models may suffer from fewer of these …
singularities and multiple optima. Growth mixture models may suffer from fewer of these …