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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Osteosarcopenic obesity (OSO) is a recently identified geriatric syndrome characterized by simultaneous presence of osteopenia/osteoporosis, sarcopenia, and increased adiposity either as overt overweight or fat infiltration into bone and...
This dissertation develops novel single random effect models as well as bivariate correlated random effects model for clustered data with bivariate mixed responses. Logit and identity link functions are used for the binary and continuous...
With ever increasing complexity of observational and theoretical data models, the sufficiency of the classical statistical techniques, designed to be applied only on vector quantities, is being challenged. Nonlinear statistical analysis...
Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials. In this article, we evaluate four test methods for biomarker identification:...
A multilevel logistic regression approach provides an attractive and practical alternative for the study of Differential Item Functioning (DIF). It is not only useful for identifying items with DIF but also for explaining the presence of...
Purpose: The purpose of this study was to examine the effect of the consumption of apples and moderate intensity resistance training, twice weekly, on blood glucose, abdominal circumference, blood lipids, blood pressure, BMI and insulin...
Determining the equivalence or non-inferiority of a new drug (test drug) with a existing treatment (reference drug) is an important topic of statistical interest. Wellek (1993) pioneered the way for log-rank based equivalence and non...
Mixture IRT modeling allows the detection of latent classes and different item parameter profile patterns across latent classes. In Rasch mixture model estimation, latent classes are assumed to follow a normal distribution with means...
In this study, we propose a robust method holding a selective shrinkage power for small area estimation with automatic random effects selection referred to as SARS. In our proposed model, both fixed effects and random effects are treated...
Multivariate meta-analysis refers to the statistical analyses of a collection of studies where at least some studies provide multiple effect-size estimates that may or may not represent multiple constructs. Multiple endpoint studies...
A class of hierarchical Bayesian models is introduced for adverse birth outcomes such as preterm birth, which are assumed to follow a conditional binomial distribution. The log-odds of an adverse outcome in a particular county, logit(p(i...
This research is motivated by an analysis of reading research data. We are interested in modeling the test outcome of ability to fluently recode letters into sounds of kindergarten children aged between 5 and 7. The data showed excessive...
The longitudinal data analysis plays an important role in a lot of applications today. It is defined by many measurements are obtained over many times. These measurements has complicated correlation structure because they are obtained...
Proteins and RNAs are molecular machines performing biological functions in the cells of all organisms. Automatic comparison and classification of these biomolecules are fundamental yet open problems in the field of Structural...
Count data are ubiquitous in modern statistical applications. How to modeling such data remains a challenging task in machine learning. In this study, we consider various aspects of statistical modeling on Poisson count data. Concerned...
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