Results 21 to 30 of about 254,793 (285)

Validating online approaches for rare disease research using latent class mixture modeling

Orphanet Journal of Rare Diseases, 2021
Background Rare disease patients are geographically dispersed, posing challenges to research. Some researchers have partnered with patient organizations and used web-based approaches to overcome geographic recruitment barriers.
Andrew A. Dwyer, Ziwei Zeng, Christopher S. Lee   +2 more
doaj   +1 more source

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

BMC Bioinformatics, 2011
Background Discovering the genetic basis of common genetic diseases in the human genome represents a public health issue. However, the dimensionality of the genetic data (up to 1 million genetic markers) and its complexity make the statistical analysis a
Leray Philippe, Sinoquet Christine, Mourad Raphaël   +2 more
doaj   +1 more source

Latent trajectory studies: the basics, how to interpret the results, and what to report [PDF]

European Journal of Psychotraumatology, 2015
Background: In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000)
Rens van de Schoot
doaj   +1 more source

A comparison of numerical splitting-based methods for Markovian dependability and performability models [PDF]

, 1998
Iterative numerical methods are an important ingredient for the solution of continuous time Markov dependability models of fault-tolerant systems. In this paper we make a numerical comparison of several splitting-based iterative methods. We consider the
Carrasco, Juan A., Suñé, Víctor
core   +1 more source

Modeling factors to various drivers' actions due to vehicle malfunctions, the integrated latent class and latent variable model

Engineering Reports, 2022
It is known that how individuals perceive the situation result in choices. However, it is unknown, how drivers process the information of vehicle malfunctions based on the situation at hand.
Mahdi Rezapour, Khaled Ksaibati
doaj   +1 more source

Class Enumeration and Parameter Recovery of Growth Mixture Modeling and Second-Order Growth Mixture Modeling in the Presence of Measurement Noninvariance between Latent Classes

Frontiers in Psychology, 2017
Population heterogeneity in growth trajectories can be detected with growth mixture modeling (GMM). It is common that researchers compute composite scores of repeated measures and use them as multiple indicators of growth factors (baseline performance ...
Eun Sook Kim, Yan Wang
doaj   +1 more source

Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students

Journal of Intelligence, 2022
Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children’s academic achievements and ability to adjust to school.
Eun-Kyoung Goh, Hyo-Jeong Jeon
doaj   +1 more source

Modeling large scale species abundance with latent spatial processes [PDF]

, 2009
Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species ...
Chakraborty, Avishek, Gelfand, Alan E., Latimer, Andrew M., Silander Jr, John A., Wilson, Adam M.   +4 more
core   +1 more source

Techniques to produce and evaluate realistic multivariate synthetic data

Scientific Reports, 2023
Data modeling requires a sufficient sample size for reproducibility. A small sample size can inhibit model evaluation. A synthetic data generation technique addressing this small sample size problem is evaluated: from the space of arbitrarily distributed
John Heine, Erin E. E. Fowler, Anders Berglund, Michael J. Schell, Steven Eschrich   +4 more
doaj   +1 more source

Gaussian process latent class choice models

Transportation Research Part C: Emerging Technologies, 2022
We present a Gaussian Process - Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes ...
Sfeir, Georges, Rodrigues, Filipe, Abou-Zeid, Maya   +2 more
openaire   +3 more sources