Factor Mixture Modeling of the Insomnia Severity Index among Psychology Clinic Outpatients
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Psychopathology and Behavioral Assessment"
DOI: 10.1007/s10862-020-09816-5
Abstract: Insomnia symptoms are common among individuals with psychiatric disorders, and associated with increased symptom severity. However, the Insomnia Severity Index (ISI) has rarely been psychometrically evaluated in a psychiatric sample. Furthermore, the latent structure of… read more here.
Keywords: severity; factor mixture; factor; mixture modeling ... See more keywords
Thresholding functional connectomes by means of mixture modeling
Sign Up to like & getrecommendations! Published in 2018 at "Neuroimage"
DOI: 10.1016/j.neuroimage.2018.01.003
Abstract: &NA; Functional connectivity has been shown to be a very promising tool for studying the large‐scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of… read more here.
Keywords: network; functional connectomes; means mixture; mixture ... See more keywords
Mixture-modeling approach reveals global and local processes in visual crowding.
Sign Up to like & getrecommendations! Published in 2022 at "Scientific reports"
DOI: 10.1038/s41598-022-10685-z
Abstract: Crowding refers to the inability to recognize objects in clutter, setting a fundamental limit on various perceptual tasks such as reading and facial recognition. While prevailing models suggest that crowding is a unitary phenomenon occurring… read more here.
Keywords: approach reveals; mixture modeling; crowding; configuration ... See more keywords
Robust mixture modeling reveals category-free selectivity in reward region neuronal ensembles.
Sign Up to like & getrecommendations! Published in 2018 at "Journal of neurophysiology"
DOI: 10.1152/jn.00808.2017
Abstract: Classification of neurons into clusters based on their response properties is an important tool for gaining insight into neural computations. However, it remains unclear to what extent neurons fall naturally into discrete functional categories. We… read more here.
Keywords: robust mixture; task; reveals category; modeling reveals ... See more keywords
An alternative classification to mixture modeling for longitudinal counts or binary measures
Sign Up to like & getrecommendations! Published in 2017 at "Statistical Methods in Medical Research"
DOI: 10.1177/0962280214549040
Abstract: Classifying patients according to longitudinal measures, or trajectory classification, has become frequent in clinical research. The k-means algorithm is increasingly used for this task in case of continuous variables with standard deviations that do not… read more here.
Keywords: classification; classification mixture; modeling longitudinal; alternative classification ... See more keywords
Identifying longitudinal patterns of CPAP treatment in OSA using growth mixture modeling: Disease characteristics and psychological determinants
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurology"
DOI: 10.3389/fneur.2022.1063461
Abstract: In this study, we aim to identify the distinct subtypes of continuous positive airway pressure (CPAP) user profiles based on the telemedicine management platform and to determine clinical and psychological predictors of various patterns of… read more here.
Keywords: mixture modeling; growth mixture; adherence; slope ... See more keywords
Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study
Sign Up to like & getrecommendations! Published in 2021 at "Frontiers in Psychology"
DOI: 10.3389/fpsyg.2021.618647
Abstract: Growth mixture models are regularly applied in the behavioral and social sciences to identify unknown heterogeneous subpopulations that follow distinct developmental trajectories. Marcoulides and Trinchera (2019) recently proposed a mixture modeling approach that examines the… read more here.
Keywords: growth; simulation study; growth mixture; approach ... See more keywords
Entropy-Based Anomaly Detection for Gaussian Mixture Modeling
Sign Up to like & getrecommendations! Published in 2023 at "Algorithms"
DOI: 10.3390/a16040195
Abstract: Gaussian mixture modeling is a generative probabilistic model that assumes that the observed data are generated from a mixture of multiple Gaussian distributions. This mixture model provides a flexible approach to model complex distributions that… read more here.
Keywords: mixture modeling; mixture; model; anomaly detection ... See more keywords