Research ArticleOpen accessPhrase in the titleInversion of Covariance Matrix for High Dimension DataSamruam ChongcharoenProblem statement: In the testing statistic problem for the mean vector of independent and identically distributed multivariate normal random vectors with unknown covariance matrix when the data has sample size less than the dimension n≤p, for example, the data came from DNA microarrays where a large number of gene expression levels are measured on relatively few subjects, the p×p sample … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 27 Jul 2011 · 7,869 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Test for Two-Sample Repeated Measures Designs: Effect of High-Dimensional DataBoonyarit Choopradit, Samruam ChongcharoenProblem statement: High-dimensional repeated measures data are increasingly encountered in various areas of modern science since classical multivariate statistics, e.g,. Hotelling’s T2, are not well defined in the case of high-dimensional data. Approach: In this study, the test statistics with no specific form of covariance matrix for analyzing high-dimensional two-sample repeated measures designs with common equal covariance are proposed. The … …Journal of Mathematics and Statistics, Vol. 7, Issue 4 · 28 Oct 2011 · 7,328 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Prediction Model for Information Anxiety of CSs Based on CMA-ES and XGBoostBin Wang, Li ShaoWith the rapid advancement of information technology, college students are increasingly facing the problem of information overload. Information anxiety, an emerging mental health issue, significantly impacts their studies, daily lives, and mental well-being. Given the limitations of existing methods in handling complex nonlinear relationships and high-dimensional data, this study proposes an efficient and accurate prediction model for college students’ information … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 1 · 21 Apr 2026 · 1,695 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit ModelsXiao ZhangMultivariate probit models have been explored foranalyzing longitudinal ordinal data. However, the inherent identification issuein multivariate probit models requires the covariance matrix of the underlyinglatent multivariate normal variables to be a correlation matrix and thushinders the development of efficient Bayesian sampling methods. It is knownthat non-identifiable models may produce Markov Chain Monte Carlo (MCMC)samplers with better convergence and mixing than … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 14 Nov 2022 · 6,044 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Test on the Multivariate Behrens–Fisher Problem in High–Dimensional Data by Block Covariance EstimationParanut Sukcharoen, Samruam ChongcharoenIn this paper, we proposed a new testing statistic for testing the equality of mean vectors from two multivariate normal populations when the covariance matrices are unknown and unequal in high–dimensional data. A new test is proposed based on the idea of keeping more information from the sample covariance matrices as much as possible. A proposed test is invariant under … …Journal of Mathematics and Statistics, Vol. 15, Issue 1 · 2 Apr 2019 · 7,369 accessesView PDFCite
Research ArticleOpen accessIn the abstractFDIR Strategy for Sensor Faults During Re-entry FlightGianfranco Morani, Mariangela Di LorenzoIn this study, a Fault Detection, Isolation and Reconfiguration (FDIR) strategy is proposed for dealing with the problem of single sensor faults during a re-entry flight. The proposed algorithms rely on a model based Fault Detection and Isolation. After the generation of a residual through a Kalman filter observer, detection is obtained via the estimation of residual signal statistics while … …American Journal of Engineering and Applied Sciences, Vol. 11, Issue 2 · 7 Apr 2018 · 8,690 accessesView PDFCite
Research ArticleOpen accessIn the abstractMonetary Policy in a Markov-Switching VECM: Implications for the Cost of Disinflation in GhanaRichard Kwabi Ayisi, Joseph AduMonetary policy assessment in Ghana has been conducted using vector auto-regression. This however, presumes stability of long run outcomes and particularly ignores monetary policy regime changes that has characterized the economy overtime. This study thus introduced the possibility of switches in the long run equilibrium in co-integrated vector auto-regression by allowing both the covariance and weighting matrix in the error-correction … …American Journal of Economics and Business Administration, Vol. 8, Issue 2 · 18 Mar 2017 · 8,548 accessesView PDFCite
Research ArticleOpen accessIn the abstractRobust Linear Discriminant AnalysisSharipah Soaad Syed Yahaya, Yai-Fung Lim, Hazlina Ali, Zurni OmarLinear Discriminant Analysis (LDA) is the most commonly employed method for classification. This method which creates a linear discriminant function yields optimal classification rule between two or more groups under the assumptions of normality and homoscedasticity (equal covariance matrices). However, the calculation of parametric LDA highly relies on the sample mean vectors and pooled sample covariance matrix which are sensitive … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 4 Jan 2017 · 7,649 accessesView PDFCite
Research ArticleOpen accessIn the abstractModelling the Error Term of Australia Gross Domestic ProductAyodele Abraham Agboluaje, Suzilah Ismail, Chee Yin YipThe main aim of this study is to model the Gross Domestic Product (GDP) with the new Combine White Noise (CWN) Model and compare the results with the Vector Autoregressive (VAR) Model and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) Model which are the existing models. The CWN model estimation yields best results with least information criteria and high log likelihood … …Journal of Mathematics and Statistics, Vol. 12, Issue 4 · 2 Nov 2016 · 6,930 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssessing Convergence of the Markov Chain Monte Carlo Method in Multivariate CaseDenismar Alves Nogueira, Thelma Safadi, Daniel Furtado Ferreira, Eric Batista FerreiraThe formal convergence diagnosis of the Markov Chain Monte Carlo (MCMC) is made using univariate and multivariate criteria. In 1998, a multivariate extension of the univariate criterion of multiple sequences was proposed. However, due to some problems of that multivariate criterion, an alternative form of calculation was proposed in addition to the two new alternatives for multivariate convergence criteria. In … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 8 Jan 2013 · 7,019 accessesView PDFCite