Here, we used hidden Markov models for fitting the DNA sequence dynamics, and developed an efficient spectral algorithm for extracting latent variable information from these models.
In particular, we use HMMs as a probabilistic generative model for DNA sequences, and develop an efficient spectral algorithm for extracting latent variable information from these models.
Regarding attrition from baseline to follow-up in a longitudinal study, this situation can be classified as missing at random (MAR) as attrition was dependent on baseline variables with information from all respondents, but not directly dependent on follow-up variables with missing information.
Statistical techniques to account for missing data, such as full information maximum likelihood and most current forms of multiple imputation, are less efficient when missingness is dependent on variables with missing data than when it is only dependent on variables with information from all participants [ 20].
†Some variables require information from both the TLC and RV CT †† Heterogeneity scores are derived by subtracting the minimum destruction score from the maximum destruction score.
Modern techniques for handling missing data (e.g. full information maximum likelihood and multiple imputation methods) are effective in adjusting for missingness that is dependent on variables with information from all participants [ 20].
If this assessment was not a full assessment (i.e., it was a truncated quarterly assessment), we completed the missing variables using information from a prior full assessment, following the procedure used by CMS to generate Resident Profile Table (RPT) records (see [ 9] for example).
Though their sequences are short and the repeat regions themselves are highly variable, sequence information from several microsatellite flanking regions can be combined for robust, informative and easily aligned characters that can help resolve difficult phylogenetic relationships.
Nonetheless, we attempted to control for smoking prevalence by creating a census-tract level variable using information from census-tract level on smoking by ethnicity, avariable from the Texas Department of State Health Service's Behavioral Risk Factor Susingllance System (Texas Department of State Health Servinformation
This is not surprising because there is no guarantee that maximum-variance predictor variables always maximize information from the dependent variable [ 73].
