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17 articles for “multivariate normal”
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Research ArticleOpen accessPhrase in the titleRisks Ratios of Shrinkage Estimators for the Multivariate Normal MeanAbdenour Hamdaoui, Nadia MezouarWe study the estimation of the mean θ of a multivariate Gaussian random variable X∼Np(θ,σ2Ip) in ℜp, σ2 is unknown and estimated by the chi-square variable S2∼σ2χn2. In this work we are interested in studying bounds and limits of risk ratios of shrinkage estimators to the maximum likelihood estimator X, when n and p tend to infinity. We recall that … …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 3 Apr 2017 · 7,108 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOne-Sided Multivariate Tests for High Dimensional DataSamruam ChongcharoenProblem statement: For a multivariate normal population with size smaller than dimension, n<p, the likelihood ratio tests of the null hypothesis that the mean vector was zero with a one-sided alternative were no longer valid because they involved with sample covariance matrix which was singular. Approach: The test statistics for one-sided multivariate hypotheses with n<p were proposed. Results: The simulation … …Journal of Mathematics and Statistics, Vol. 8, Issue 2 · 20 Jun 2012 · 7,261 accessesView PDFCite
Research ArticleOpen accessIn the keywordsModelling the Effect of Salinity on the Multivariate Distribution of a Water Quality IndexDaniela cocchi, Michele scagliariniSalinity is a tracer of water and nutrient flows and is employed in studies on coastal environment to establish the coastal or remote origins of pollutants and nutrients. The present article analyses the effects of salinity on the multivariate distribution of the Trophic Index TRIX. The approach taken is based on a multivariate nested model with salinity as an explicative … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 5,915 accessesView PDFCite
Research ArticleOpen accessIn the abstractMultivariate Option Pricing with Gaussian Mixture Distributions and Mixed CopulasJimbo Henri Claver, Tatanfack Emerson, Shu FelixRecently, it has been reported that the hypothesis proposed by the classical black scholes model to price multivariate options in finance were unrealistic, as such, several other methods have been introduced over the last decades including the copulas methods which uses copulas functions to model the dependence structure of underlying assets. However, the previous work did not take into account … …Journal of Mathematics and Statistics, Vol. 19, Issue 1 · 3 Apr 2023 · 6,511 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 abstractEffects of Aerobic Exercise on PPAR-γ Expression in Adipose Tissue and RT-PCR Detection AnalysisXiaodong Zhan, Chenguang Bi, Jie WeiThis study aimed to examine the effect of organized aerobic exercise on the expression level of Peroxisome Proliferator-Activated Receptor gamma (PPAR-γ) gene in adipose tissue by performing digital variant of RT-PCR, Droplet Digital PCR (ddPCR) which is a highly sensitive method for counting the gene copy numbers. The proposed scheme consists of three-stage analytical procedure, namely, Subphase I Controlled Aerobic … …American Journal of Biochemistry and Biotechnology, Vol. 22, Issue 3 · 29 Sep 2026 · 580 accessesView PDFCite
Research ArticleOpen accessIn the abstractComparison of Probit and Logistic Regression Models in the Analysis of Dichotomous OutcomesAmrutha Jose, Mariyamma Philip, Lavanya Tumkur Prasanna, M. ManjulaProbit and logistic regression models are members of the family of generalized linear models, used for estimating the functional relationship between the dichotomous dependent and independent variables. The current study is designed to find the performance of logistic and probit regression models in different conditions under multivariate normality. The objective of the study is to compare the performance of probit … …Current Research in Biostatistics, Vol. 10, Issue 1 · 26 Mar 2020 · 17,036 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 abstractHand Anthropometric Data for Saudi Arabia Engineering Students of Aged 20-26 Years at King Khalid UniversityMohamed A.A. MansourAnthropometric data plays a significant role in the effective and accurate design of various devices and machines. The inclusion of anthropometric data helps ensure that devices or machines are safe, user-friendly and highly productive and efficient. In this study, 56 hand dimensions based on 266 Saudi Arabian inter-university adult males aged 20-26 years are described in terms of statistics, bivariate … …American Journal of Engineering and Applied Sciences, Vol. 9, Issue 4 · 27 Sep 2016 · 8,691 accessesView PDFCite
Research ArticleOpen accessIn the abstractSMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACHRita Diana, I. Nyoman Budiantara, Purhadi, Satwiko DarmestoSemiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 12 Jun 2013 · 7,252 accessesView PDFCite