Adriana Irawati Nur Ibrahim

Mean Monte Carlo Finite Difference Method for Random Sampling of a Nonlinear Epidemic System

In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to simulate values of the variable coefficients as random sampling instead being limited as real values with respect to time. The mean of the n final solutions via this integrated technique, named in short as mean Monte Carlo finite difference (MMCFD) method, represents the final solution of the system. This method is proposed for the first time to calculate the numerical solution obtained for each subpopulation as a vector distribution. The numerical outputs are tabulated, graphed, and compared with previous statistical estimations for 2013, 2015, and 2030, respectively. The solutions of FD and MMCFD are found to be in good agreement with small standard deviation of the means, and small measure of difference. The new MMCFD method is useful to predict intervals of random distributions for the numerical solutions of this epidemiology model with better approximation and agreement between existing statistical estimations and FD numerical solutions.

Multivariate statistical analysis for identifying water quality and hydrogeochemical evolution of shallow groundwater in Quaternary deposits in the Lower Kelantan River Basin, Malaysian Peninsula

A long-term hydrogeochemical data set is used in this study to evaluate the water quality and hydrogeochemical evolution of shallow groundwater in a Quaternary deposit. A multivariate statistical method, hierarchical cluster analysis (HCA), is applied to overcome the problem of a large number of data points in the integration, interpretation and representation of the results. HCA is applied to a subgroup of the hydrogeochemical data set to evaluate their usefulness to classify the groundwater bodies. This subgroup consists of 27 groundwater wells and 15 variables [pH, total dissolved solids, electrical conductivity (EC), Na+, Ca2+, Mg2+, K, HCO3−, Cl−, SO42−, Fe, Mn, NH4, NO3− and SiO2]. Only 12 chemical variables were used for the analysis. Four clusters have been identified: C1–C4, with two main prevalent facies, Na–HCO3 and Ca–HCO3. The hydrogeochemical evolution of shallow groundwater is governed by the processes of precipitation, weathering, dissolution and ion exchange.

A New Discordancy Test in Circular Data Using Spacings Theory

In this article, we propose a new test of discordancy based on spacing theory in circular data. The test should provide a good alternative to existing tests of discordancy for detecting single or well-separated multiple outliers. On top of that, the new method can be generalized to identify a patch of outliers in data. The percentage points are calculated and the performance is examined. We first investigate the performance of the test for detecting a single outlier and show that the new test performs well compared to other known tests. We then show that the generalized test works well in detecting a patch of outliers in the data. As an illustration, a practical example based on an eye dataset obtained from a glaucoma clinic at the University of Malaya Medical Center, Malaysia is presented.

The Correlated Bivariate Noncentral F Distribution and Its Application

This article proposes the singly and doubly correlated bivariate noncentral F (BNCF) distributions. The probability density function (pdf) and the cumulative distribution function (cdf) of the distributions are derived for arbitrary values of the parameters. The pdf and cdf of the distributions for different arbitrary values of the parameters are computed, and their graphs are plotted by writing and implementing new R codes. An application of the correlated BNCF distribution is illustrated in the computations of the power function of the pre-test test for the multivariate simple regression model (MSRM).

A new discordancy test on a regression for cylindrical data

A cylindrical data set consists of circular and linear variables. We focus on developing an outlier detection procedure for cylindrical regression model proposed by Johnson and Wehrly (1978) based on the k-nearest neighbour approach. The procedure is applied based on the residuals where the distance between two residuals is measured by the Euclidean distance. This procedure can be used to detect single or multiple outliers. Cut-off points of the test statistic are generated and its performance is then evaluated via simulation. For illustration, we apply the test on the wind data set obtained from the Malaysian Meteorological Department.

Bayesian approach to errors-in-variables in count data regression models with departures from normality and overdispersion

ABSTRACT In most practical applications, the quality of count data is often compromised due to errors-in-variables (EIVs). In this paper, we apply Bayesian approach to reduce bias in estimating the parameters of count data regression models that have mismeasured independent variables. Furthermore, the exposure model is misspecified with a flexible distribution, hence our approach remains robust against any departures from normality in its true underlying exposure distribution. The proposed method is also useful in realistic situations as the variance of EIVs is estimated instead of assumed as known, in contrast with other methods of correcting bias especially in count data EIVs regression models. We conduct simulation studies on synthetic data sets using Markov chain Monte Carlo simulation techniques to investigate the performance of our approach. Our findings show that the flexible Bayesian approach is able to estimate the values of the true regression parameters consistently and accurately.

A new test of discordancy in cylindrical data

ABSTRACT Cylindrical data are bivariate data from the combination of circular and linear variables. However, up to now no work has been done on the detection of outlier in cylindrical data. We introduce a definition of outlier for cylindrical data and present a new test of discordancy to detect outlier in this type of data, based on the k-nearest neighbor’s distance. Cut-off points of the new test statistic based on the Johnson-Wehrly distribution are calculated and its performance is examined using simulation. A practical example is presented using wind speed and wind direction data obtained from the Malaysian Meteorological Department.

Bayesian approach to errors-in-variables in regression models

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

Markov chain Monte Carlo simulation for Bayesian Hidden Markov Models

A hidden Markov model (HMM) is a mixture model which has a Markov chain with finite states as its mixing distribution. HMMs have been applied to a variety of fields, such as speech and face recognitions. The main purpose of this study is to investigate the Bayesian approach to HMMs. Using this approach, we can simulate from the parameters’ posterior distribution using some Markov chain Monte Carlo (MCMC) sampling methods. HMMs seem to be useful, but there are some limitations. Therefore, by using the Mixture of Dirichlet processes Hidden Markov Model (MDPHMM) based on Yau et. al (2011), we hope to overcome these limitations. We shall conduct a simulation study using MCMC methods to investigate the performance of this model.

Numerical solution for weight reduction model due to health campaigns in Spain

Transition model between three subpopulations based on Body Mass Index of Valencia community in Spain is considered. No changes in population nutritional habits and public health strategies on weight reduction until 2030 are assumed. The system of ordinary differential equations is solved using Runge-Kutta method of higher order. The numerical results obtained are compared with the predicted values of subpopulation proportion based on statistical estimation in 2013, 2015 and 2030. Relative approximate error is calculated. The consistency of the Runge-Kutta method in solving the model is discussed.