Factor analysis (FA) is a statistical technique to explain the correlation structure among observed variables. It can e.g. be useful in the analysis of Patient Reported Outcomes, which typically use multiple questions to measure a single concept. Maximum likelihood factor analysis (ML-FA) relies on large sample theory, and it is consequently often recommended to use it only in large samples (e....

We consider factor analysis when we assume the distribution form is known up to its mean and variance. A prior is placed on the mean and variance and then inference is made as to whether or not any latent factors exist. Inference is carried out by comparing the concentrations of the prior and posterior about various subsets of the parameter space that are specified by hypothesizing factor struc...

aim : the aim of this study is to estimate gastric cancer (gc) mortality rate for iranian population, using bayesian approach in order to revise the existing classification which is thought to be a misclassification. background : gastric cancer (gc) is an important cause of mortality among many other types of cancer. data on cancer mortality can be used to guide policy makers in order to setup ...

aim : the aim of this study is to estimate oral cavity cancer mortality for iranian population, using bayesian approach in order to revise this misclassification. background : mortality is a familiar projection to address the burden of cancers, but according to iranian death registry, about 20% death statistics were still recorded in misclassified categories. patients and methods : we analyzed ...

background: high-risk unsafe behaviors (hrubs) have been known as the main cause of occupational accidents. considering the financial and societal costs of accidents and the limitations of available resources, there is an urgent need for managing unsafe behaviors at workplaces. the aim of the present study was to find strategies for decreasing the rate of hrubs using an integrated approach of s...

the problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance. we discuss a bayesian approach in the context of statistical process control: at an unknown time $tau$, the process behavior changes and the distribution of the data changes from p0 to p1. two cases are considered: (i) p0 and p1 are fu...