Fitting Gaussian mixture models on incomplete data.

ABSTRACT:

Background

Bioinformatics investigators often gain insights by combining information across multiple and disparate data sets. Merging data from multiple sources frequently results in data sets that are incomplete or contain missing values. Although missing data are ubiquitous, existing implementations of Gaussian mixture models (GMMs) either cannot accommodate missing data, or do so by imposing simplifying assumptions that limit the applicability of the model. In the presence of missing data, a standard ad hoc practice is to perform complete case analysis or imputation prior to model fitting. Both approaches have serious drawbacks, potentially resulting in biased and unstable parameter estimates.

Results

Here we present missingness-aware Gaussian mixture models (MGMM), an R

SUBMITTER: McCaw ZR 

PROVIDER: S-EPMC9158227 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature