Sentence examples for mixture modeling and from inspiring English sources

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

The proposed model utilizes mixture modeling and nonparametric smoothing methods to capture hypothesized latent subgroup features of the FSH longitudinal trajectory; and simultaneously studies the prognostic value of these latent subgroup features to predict age at FMP.

Dr. Bowers studies these domains through the application of Intensive Longitudinal Data analysis (ILD), data science, and big data analytics, such as data visualization analytics, multilevel and growth mixture modeling, and cluster analysis heatmap data dashboards.

Four antisocial behavior trajectory groups were identified among females and males using general growth mixture modeling and included life-course persistent (LCP), adolescent-onset, childhood-limited, and low trajectory groups.

MouseDivGeno uses a novel combination of Gaussian mixture modeling and hierarchical clustering followed by a VINO detection step (details in METHODS).

A two-step analytic approach was used, first, to identify distinct glycemic control classes using latent growth mixture modeling and, second, to examine trajectories of lower-extremity function based on these classes using path analysis.

Data were analyzed using growth mixture modeling (GMM) and multinomial logistic regression.

Many methods have been proposed to solve this problem, including selective taxon sampling, removal of fast-evolving proteins and saturated sites [ 42- 44], accounting for covarion shifts [ 44], amino acid profile mixture modeling [ 45], branch length mixture modeling [ 17] and rare genomic changes of conserved amino acids-multiple substitutions [ 46].

To further examine heterogeneity in gene expression, we characterized modality of expression profiles across host-pathogen single cells, and then compared these distributions between 2 and 4 h using a normal mixture model and Bayesian modeling framework as implemented in scDD28 (Methods).

We build on Bayesian simulation methods and mixture modeling to define the approach to linking mechanistic mathematical models of network dynamics to snapshot data, using a toggle switch example integrating simulated and real data as context.

We compared olfactory performance and performed mixture modeling to identify possible subgroups of olfactory performance in LRRK2 PD and nonmanifesting carriers.