The complex Pearson (CP) distributions are a family of probability models for count data generated by the Gaussian hypergeometric function with arguments. triparametric (CTP) distribution and its biparametric versions, (CBP) extended Waring (EBW) distributions, belong to this family. They all have explicit expressions mass (pmf), generating moments, so they easy handle from computational point ...

background and objectives: the prevalence of diabetes mellitus (insulin resistance) in individuals with down's syndrome is more than healthy people, which is possibly because of the high prevalence of obesity among them. adiponectin is one of adipose tissue-secreted adipocytokines, which plays a role in pathogenesis of obesity and insulin resistance. this study was carried out with the aim of d...

This chapter is dedicated to scope of the application of Importance Sampling Techniques to the design phase of Neyman-Pearson Neural Detectors. This phase usually requires the application of MonteCarlo trials in order to estimate some performance parameters. The classical Monte-Carlo method is suitable to estimate high event probabilities but not suitable to estimate very low event probabilitie...

Gene coexpression networks inferred by correlation from high-throughput profiling such as microarray data represent simple but effective structures for discovering and interpreting linear gene relationships. In recent years, several approaches have been proposed to tackle the problem of deciding when the resulting correlation values are statistically significant. This is most crucial when the n...

One of the famous controversies in statistics is the dispute between Fisher and Neyman-Pearson about the proper way to conduct a test. Hubbard and Bayarri (2003) gave an excellent account of the issues involved in the controversy. Another famous controversy is between Fisher and almost all Bayesians. Fisher (1956) discussed one side of these controversies. Berger’s Fisher lecture attempted to c...

A symmetric version of the Neyman-Pearson test is developed for discriminating between sets of hypotheses and is extended to encompass a new formulation of the problem of parameter estimation based on finite data sets. Such problems can arise in distributed sensing and localization problems in sensor networks, where sensor data must be compressed to account for communication constraints. In thi...