Aim The aim of this study was to investigate the impact of diabetes and hypertension on colorectal cancer (CRC) mortality. Background One of the methodology in epidemiological studies is to use self-report questionnaires to gather data, this is the easiest and cheapest method but involves with misclassification bias. We use robust Bayesian adjustment to correct this bias. Methods One of the...

Genetic association studies lead to simultaneous categorical data analysis. The sample for every genetic locus consists of a contingency table containing the numbers of observed genotype-phenotype combinations. Under case-control design, the row counts of every table are identical and fixed, while column counts are random. The aim of the statistical analysis is to test independence of the pheno...

The efficient long term management of large-scale public funded assets is an area of growing importance. Ageing infrastructure, growth, and limited capital all result in the need for more robust and rigorous methodology to prioritise rehabilitation and renewal decisions and as importantly, to forecast future expenditure requirements. The overall objective of this research is to develop a Bayesi...

This paper introduces a novel class of Bayesian models for multivariate time series analysis based on a synthesis of dynamic linear models and graphical models. The synthesis uses sparse graphical modelling ideas to introduce structured, conditional independence relationships in the time-varying, cross-sectional covariance matrices of multiple time series. We define this new class of models and...

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bay...

In part I of this two-part study, we introduced a new optimal Bayesian classification methodology that utilizes the same modeling framework proposed in Bayesian minimum-mean-square error (MMSE) error estimation. Optimal Bayesian classification thus completes a Bayesian theory of classification, where both the classifier error and our estimate of the error may be simultaneously optimized and stu...

text classification is an important research field in information retrieval and text mining. the main task in text classification is to assign text documents in predefined categories based on documents’ contents and labeled-training samples. since word detection is a difficult and time consuming task in persian language, bayesian text classifier is an appropriate approach to deal with different...

In this paper we introduce a stochastic optimization method based ona mixed Bayesian/frequentist approach to a sample size determinationproblem in a clinical trial. The data are assumed to come from a nor-mal distribution for which both the mean and the variance are unknown.In contrast to the usual Bayesian decision theoretic methodology, whichassumes a single decision maker, our method recogni...

This paper presents clustering techniques that partition temporal data into homogeneous groups, and constructs state based proles for each group in the hidden Markov model (HMM) framework. We propose a Bayesian HMM clustering methodology that improves upon existing HMM clustering by incorporating HMM model size selection into clustering control structure to derive better cluster models and part...

Vector autoregressions (VAR) are extensively used to model economic time series. The large number of parameters is the main diicult with VAR models, however. To overcome this, Litterman (1986) suggests to use a Bayesian strategy to estimate the VAR, equation by equation, where, a priori, the lags have decreasing importance (known as Litterman Prior). In this paper, a VAR model is analyzed throu...