Model selection in multivariate adaptive regressions splines (MARS) using alternative information criteria.

Multivariate Adaptive Regression Splines (MARS) is a useful non-parametric regression analysis method that can be used for model selection in high-dimensional data. Since MARS can identify and model complex, non-linear relationships between the dependent variable and independent variables without requiring any assumptions, it has advantage over simple linear regression techniques. Also, for simplifying the model building process and preventing overfitting, MARS can select automatically the variables to be included in the model, which is useful for datasets with many variables.

A Novel Expert System for Diagnosis of Iron Deficiency Anemia.

Diagnosis of a disease is one of the most important processes in the field of medicine. Thus, computer-aided detection systems are becoming increasingly important to assist physicians. The iron deficiency anemia (IDA) is a serious health problem that requires careful diagnosis.

Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models.

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations.

A Bayesian Approach for Evaluation of Determinants of Health System Efficiency Using Stochastic Frontier Analysis and Beta Regression.

In today's world, Public expenditures on health are one of the most important issues for governments. These increased expenditures are putting pressure on public budgets. Therefore, health policy makers have focused on the performance of their health systems and many countries have introduced reforms to improve the performance of their health systems.

Bayesian hierarchical modeling for categorical longitudinal data from sedation measurements.

We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sedation level of patients is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response, and then subsequent terms are introduced.

Comparing models of the effect of air pollutants on hospital admissions and symptoms for chronic obstructive pulmonary disease.

There is an increasing interest in the use of hospital admission for Chronic obstructive pulmonary disease (COPD) in studies of short-term exposure effects attributed to air pollutants. However, little is known about the effect of air pollutants on COPD symptoms. This study was undertaken to determine whether there was an association between air pollutant levels and both hospital admissions and symptoms for COPD.