Stochastic ordering constraint for ordered extremes

The constraint of two ordered extreme minima random variables when one variable is consider to be stochastically smaller than the other one has been carried out in this article. The quantile functions of the probability distribution have been used to establish partial ordering between the two variables. Some extensions and generalizations are given for the stochastic ordering using the important of sign of the shape parameter

Markov chain Monte Carlo convergence diagnostics for Gumbel model

Markov chain Monte Carlo (MCMC) has been widely used in Bayesian analysis for the analysis of complex statistical models. However, there are some isues on determining the convergence of this technique. It is difficult to determine the length of draws to make sure that the sample values converge to the stationary distribution and the number of n iterations should be discarded before the chain converge to the stationary distribution. Convergence diagnostics help to decide whether the chain converges during a particular sample run. Gelman and Rubin diagnostic is the most widely used method for convergence test. The MCMC technique, Metropolis-Hastings algorithm is used for posterior inferences of Gumbel distribution simulated data

Bayesian inference for the bivariate extreme model

The bivariate extreme distribution based on logistic dependence function is used to model the extreme observations of two different variables. The model is used in a Bayesian framework where no information of prior is available on unknown model parameters. Maximum likelihood method and a Markov chain Monte Carlo (MCMC) technique, Multiple-try Metropolis algorithm are implemented into the data analysis. MTM algorithm is the new alternative in the field of Bayesian extremes for summarizing the posterior distribution. Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. The proposed theoretical methods apply to extreme particulate matter data from two air monitoring stations in Johor

On the use of Bayesian network classifiers to classify patients with peptic ulcer among upper gastrointestinal bleeding patients

A Bayesian network classifier is one type of graphical probabilistic models that is capable of representing relationship between variables in a given domain under study. We consider the naive Bayes, tree augmented naive Bayes (TAN) and boosted augmented naive Bayes (BAN) to classify patients with peptic ulcer disease among upper gastro intestinal bleeding patients. We compare their performance with IBk and C4.5. To identify relevant variables for peptic ulcer disease, we use some methodologies for attributes subset selection. Results show that, blood urea nitrogen, hemoglobin and gastric malignancy are important for classification. BAN achieves the best accuracy of 77.3 and AUC of (0.81) followed by TAN with 72.4 and 0.76 respectively among Bayesian classifiers. While the accuracy of the TAN is improved with attribute selection, the BAN and IBK are better off without attribute selection

Performance of Three Popular Statistical Softwares in Generating Random Numbers

Random numbers are essential ingredients in a statistical analysis, They can be generated easily through statistical software packages, Each statistical package varies in terms of performance. Our objective is to compare the performances of three statistical software packages, namely R, SAS and SPSS based on their accurateness and time consumption in generating and analyzing random numbers. We obtain some estimated statistics to assess their accuracies. Comparison of the times in generating the random numbers is also observed

Prediction of vertical jump height from anthropometric factors in male and female martial arts athletes

BACKGROUND: Vertical jump is an index representing leg/kick power. The explosive movement of the kick is the key to scoring in martial arts competitions. It is important to determine factors that influence the vertical jump to help athletes improve their leg power. The objective of the present study is to identify anthropometric factors that influence vertical jump height for male and female martial arts athletes. METHODS: Twenty-nine male and 25 female athletes participated in this study. Participants were Malaysian undergraduate students whose ages ranged from 18 to 24 years old. Their heights were measured using a stadiometer. The subjects were weighted using digital scale. Body mass index was calculated by kg/m(2). Waist-hip ratio was measured from the ratio of waist to hip circumferences. Body fat % was obtained from the sum of four skinfold thickness using Harpenden callipers. The highest vertical jump from a stationary standing position was recorded. The maximum grip was recorded using a dynamometer. For standing back strength, the maximum pull upwards using a handle bar was recorded. Multiple linear regression was used to obtain the relationship between vertical jump height and explanatory variables with gender effect. RESULTS: Body fat % has a significant negative relationship with vertical jump height (P < 0.001). The effect of gender is significant (P < 0.001): on average, males jumped 26% higher than females did. CONCLUSION: Vertical jump height of martial arts athletes can be predicted by body fat %. The vertical jump for male is higher than for their female counterparts. Reducing body fat by proper dietary planning will help to improve leg power

Survival Study of Extreme Record

This paper describes the survival study of extreme record of athletics performance. Two area of statistics are used to model and check the best model for the athletics data. We make use of the extreme value theory for minima and utilized the facility provided by the Kaplan-Meier to develop new goodness-of-fit test method via graphical approaches

Bayesian network modelling of upper gastrointestinal bleeding

Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation

Effect of missing value methods on Bayesian network classification of hepatitis data

Missing value imputation methods are widely used in solving missing value problems during statistical analysis. For classification tasks, these imputation methods can affect the accuracy of the Bayesian network classifiers. This paper study’s the effect of missing value treatment on the prediction accuracy of four Bayesian network classifiers used to predict death in acute chronic Hepatitis patients. Missing data was imputed using nine methods which include, replacing with most common attribute,support vector machine imputation (SVMI), K-nearest neighbor (KNNI), Fuzzy K-means Clustering (FKMI), K-means Clustering Imputation (KMI), Weighted imputation with K-Nearest Neighbor (WKNNI), regularized expectation maximization (EM), singular value decomposition (SVDI), and local least squares imputation (LLSI). The classification accuracy of the naive Bayes (NB), tree augmented naive Bayes (TAN), boosted augmented naive Bayes (BAN) and general Bayes network classifiers (GBN)were recorded. The SVMI and LLSI methods improved the classification accuracy of the classifiers. The method of ignoring missing values was better than seven of the imputation methods. Among the classifiers, the TAN achieved the best average classification accuracy of 86.3% followed by BAN with 85.1%