Sentence examples for mixture modeling with from inspiring English sources

The performances of fit indices used for model selection in cross-sectional mixture modeling with nonnormally distributed indicators were examined in two studies using Monte Carlo methods.

The adaptive image processing and transmission scheme based on generalized Gaussian mixture modeling with opportunistic networking is proposed and used to provide the robust and effective image communication in WSNs, which is shown in Section 5. Experiment results are given in Section 6.

For instance, different number of replicate arrays were used (three by Morris et al. vs. six in our study), different types of arrays and hybridization conditions (separate vs. competitive hybridization, total IP-ed RNA vs. amplified mRNA and oligo- vs. cDNA-arrays) and different statistical analyses (Gaussian mixture modeling with log of odds (LOD) scores vs. SAM).

Using mixture modeling with latent classes in a very large sample starting at age 20, Feldt et al. [ 41] could show that SOC has a general increasing trend, independent of age.

The utility of this dataset has been shown in previous reports where we have used it to investigate reliability and confounding factors in single subject fMRI [ 1], and to develop a new adaptive thresholding method that combines Gamma-Gaussian mixture modeling with topological thresholding to improve the reliability of cluster delineation [ 2].

We examined the development of alcohol use and positive alcohol outcome expectancies in parallel, using growth mixture modeling to identify classes with different trajectories of alcohol use and expectancies.

Both 1PL and 2PL models of these kinds can be implemented in generalized linear mixed or mixture modeling software with EM or maximum likelihood estimation (e.g., Zheng & Rabe-Hesketh, 2007; Vermunt & Magidson, 2008; von Davier, Xu & Carstensen, 2011).

Individual-based growth mixture modeling combined with interplay matrix was used to identify the latent trajectory patterns in terms of both the negative and positive symptoms.

In this study, we used latent variable mixture modeling to: (1) identify distinct classes of individuals with unique, class-specific patterns of injection risk behavior, and (2) test variation in the effectiveness of the intervention across these risk classes.

Two approaches have been described to impute nonignorable nonresponse: mixture modeling and selection modeling.

There are more sophisticated modeling methods available for clinical or life course trajectories that do include reference to the longitudinal nature of the data, such as latent class growth analysis and latent class growth mixture modeling [ 30] but comparison with these techniques was beyond the scope of our study.