Non-parametric Treatment Comparison for Current Status Data

Current status data occur in many studies and in this case, each subject is observed only once. Furthermore, the distributions of observation times may be different for subjects in different treatment groups. This paper focuses on current status recurrent event data that concern occurrence rates of certain recurrent events such as disease infections and discuss non parametric comparison of several treatment groups. For the problem, two new tests procedures are proposed and a simulation study is conducted and shows that they are more efficient than the existing ones. An illustrative example on lung tumors is provided.

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Bayesian Regression Analysis of Correlates of Modern Contraceptive Method Usage: A Case Study in Hawassa City, Ethiopia

Despite widespread adoption of family planning in the developing world contraceptive use is still very low in sub-Saharan Africa including Ethiopia and in other regions. The general objective of this study was identifying the socioeconomic factors of modern contraceptive methods usage among married women of reproductive ages (15-49years old) in Hawassa city. From a total 990 sampled married women about 57.9% (573) were modern contraception methods users. 

Bayesian logistic regression procedure was adopted to make inference about the parameters of a logistic regression model. The purpose of this method is generating the posterior distribution of the unknown parameters given both the data and some prior density for the unknown parameters. Bayesian inference for logistic regression models is derived applying a Markov Chain Monte Carlo algorithm to simulate from the joint posterior distribution of the regression and the link parameters.

Methods for the Analysis of Missing Data in FMRI Studies

Functional neuroimaging has provided fundamental advances in our understanding of human brain function and is increasingly used clinically for defining atypical function and surgical planning. For example, functional imaging with blood oxygenation level dependent (BOLD) contrast as a response measure is used as a clinical tool for defining atypical development, pathology, surgical planning, and evaluating treatment outcomes. 

Despite years of statistical advances in the analysis of complete whole brain data, there has been a limited statistical advance toaddress the pronounced missingness in many functional imaging studies that uselarge discovery or small clinical case data. For example, functional magnetic resonance imaging (fMRI) analyses do not always include the entire brain due to image acquisition space limitations and susceptibility artifacts (a loss and spatial distortion of signal that results from a disruption in the magnetic field). 

Fast Computation of Significance Threshold in QTL Mapping of Dynamic Quantitative Traits

Functional Mapping is a popular statistical method in QTL mapping studies for longitudinal data. The threshold for declaring statistical significance of a QTL is commonly obtained through permutation tests, which can be time consuming. 

To improve the computational efficiency of a permutation test of mixture models used in Functional Mapping, we first quantified the correlation between QTL and longitudinal data, using a curve clustering method. Then, the QTL s which are highly correlated with the outcome were computed in the improved permutation tests. As a result, it reduces the amount of computation in permutation tests and speeds up the computation for Functional Mapping analysis. Simulation studies and real data analysis were conducted to demonstrate that the proposed approach can greatly improve the computational efficiency of QTL mapping without loss of accuracy.

A Pass to Variable Selection

Many regularized procedures produce sparse solution and therefore are sometimes used for variable selection in linear regression. It has been showed that regularized procedures are more stable than subset selection. Such procedures include LASSO, SCAD, and adaptive LASSO, to name just a few. However, their performance depends crucially on the tuning parameter selection. 

For the purpose of prediction, popular methods for the tuning parameter selection include Cp, cross-validation, and generalized cross-validation. For the purpose of variable selection, the most popular method for the tuning parameter selection is BIC. The selection consistencies of BIC for some regularized procedures have been shown. (Here the selection consistency means that the probability of selecting the data generating model is tending to one when the sample size goes to infinity, assuming that the data generating model is a subset of the full model.) 

Comprehension Process Overview

The thinking mode by images association is a reflection of the circulation of what we name "our awareness" in the set of sequences of memories corresponding to the perceptions of our senses: visual images, tactile sensations, sounds, etc., the complete set being stored in recoverable order of occurrence in various areas of the neocortex, each area corresponding to one of our senses. The sequences of events stored in the various neocortex areas are also mutually interconnected in such a way that we can easily access what we can remember of these sensory perceptions for any specific past event.

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