A Bayesian nonparametric method for prediction in EST analysis

Bayesian Prediction in Spatial Data Analysis

The Prediction Model for Bankruptcy Risk by Bayesian Method

The importance of predicting bankruptcy risk of firms is increasing because of later financial crisis. Despite practical researchers trying to present models for predicting this risk, it seems that an optimum and acceptable model that is reliable for financial statement users and auditors in order to increase their ability in decision making and professional judgment has not been presented yet....

A nonparametric Bayesian prediction interval for a finite population mean

Given a sample from a finite population, we provide a nonparametric Bayesian prediction interval for a finite population mean when a standard normal assumption may be tenuous. We will do so using a Dirichlet process (DP), a nonparametric Bayesian procedure which is currently receiving much attention. An asymptotic Bayesian prediction interval is well known but it does not incorporate all the fe...

A Bayesian Nonparametric Approach for Comparing Clustering Structures in EST Libraries

Inference for Expressed Sequence Tags (ESTs) data is considered. We focus on evaluating the redundancy of a cDNA library and, more importantly, on comparing different libraries on the basis of their clustering structure. The numerical results we achieve allow us to assess the effect of an error correction procedure for EST data and to study the compatibility of single EST libraries with respect...

A non-EST-based method for exon-skipping prediction.

It is estimated that between 35% and 74% of all human genes can undergo alternative splicing. Currently, the most efficient methods for large-scale detection of alternative splicing use expressed sequence tags (ESTs) or microarray analysis. As these methods merely sample the transcriptome, splice variants that do not appear in deeply sampled tissues have a low probability of being detected. We ...