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112 results for “Linear Regression Model”
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Research ArticleOpen accessIn the abstractE-loyalty and Business to Business (B2B) E-Commerce Success: Applied Study on Sample of E-Retailers in Amman City, JordanAhed Al-Haraizah, Baha`a Al-NadyThis research paper investigates the antecedents and consequences of customer loyalty in an online business-to-business e-commerce context. Moreover, this research identifies two key factors of e-loyalty (attitudinal and behavioral loyalty) that potentially impact business to business e-commerce success within e-retailers in Amman city. Therefore, the aim of this research paper is to develop a comprehensive research model utilized for discovering … …American Journal of Economics and Business Administration, Vol. 7, Issue 3 · 2 Sep 2015 · 7,872 accessesView PDFCite
Research ArticleOpen accessIn the abstractGenetic Algorithm for Variable and Samples Selection in Multivariate Calibration ProblemsKelton de Souza Santiago, Anderson Silva Soares, Telma Woerle de Lima et al.One of the main problems of quantitative analytical chemistry is to estimate the concentration of one or more species from the values of certain physicochemical properties of the system of interest. For this it is necessary to construct a calibration model, i.e., to determine the relationship between measured properties and concentrations. The multivariate calibration is one of the most successful … …Journal of Computer Science, Vol. 11, Issue 4 · 8 Jun 2015 · 7,381 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractMODEL SELECTION VIA ROBUST VERSION OF R-SQUAREDShokrya SalehR-squared (R2) is a popular method for variable selection in linear regression models. R2 based on Least Squares (LS) regression minimizes the sum of the squared residuals; LS is sensitive to outlier observation. Alternative criterion based on M-estimators, which is less sensitive to outlying observation has been proposed. In this study explicit expression for such criterion is obtained when the … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 9 Oct 2014 · 7,460 accessesView PDFCite
Research ArticleOpen accessIn the abstractCOLLABORATION OF STATISTICAL METHODS IN SELECTING THE CORRECT MULTIPLE LINEAR REGRESSIONSAli Hussein Al-MarshadiThis article considers the analysis of Multiple Linear Regressions (MLRs) that are essential statistical method for the analysis of medical data in various fields of medical research like prognostic studies, epidemiological risk factor studies, experimental studies, diagnostic studies and observational studies. An approach is used in this article to select the "true" regression model with different sample sizes. We used … …Current Research in Biostatistics, Vol. 4, Issue 2 · 15 Sep 2014 · 7,791 accessesView PDFCite
Research ArticleOpen accessIn the abstractSIMULTANEOUS OPTIMISATION OF DRYING PARAMETERS OF CERAMIC BODIESSalah Mahmoudi, Ezzeddine Srasra, Fouad ZargouniThis study deals with the mineralogical, physico-chemical and geotechnical analyses of representative Aptian clays in the north-east of Tunisia. X-ray diffraction reveals a predominance of illite (50-60 wt.%) associated to kaolinite and interstratified illite/smectite. The accessory minerals detected in raw materials are quartz, calcite and Na-feldspar. The average amounts of silica, alumina and alkalis are 52, 20 and 3.5 wt.%, … …Current Research in Geoscience, Vol. 4, Issue 1 · 1 Jul 2014 · 6,991 accessesView PDFCite
Research ArticleOpen accessIn the abstractGAUSSIAN COPULA MARGINAL REGRESSION FOR MODELING EXTREME DATA WITH APPLICATIONSutikno, Heri Kuswanto, Iis Dewi RatihRegression is commonly used to determine the relationship between the response variable and the predictor variable, where the parameters are estimated by Ordinary Least Square (OLS). This method can be used with an assumption that residuals are normally distributed (0, σ2). However, the assumption of normality of the data is often violated due to extreme observations, which are often found … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 4 Apr 2014 · 7,620 accessesView PDFCite
Research ArticleOpen accessIn the abstractPERFORMANCE OF RIDGE REGRESSION ESTIMATOR METHODS ON SMALL SAMPLE SIZE BY VARYING CORRELATION COEFFICIENTS: A SIMULATION STUDYAnwar Fitrianto, Lee Ceng YikWhen independent variables have high linear correlation in a multiple linear regression model, we can have wrong analysis. It happens if we do the multiple linear regression analysis based on common Ordinary Least Squares (OLS) method. In this situation, we are suggested to use ridge regression estimator. We conduct some simulation study to compare the performance of ridge regression estimator … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 3 Jan 2014 · 8,540 accessesView PDFCite
Research ArticleOpen accessIn the abstractEMPIRICAL DETERMINATION OF THE AVERAGE ANNUAL RUNOFF COEFFICIENT IN THE MEDITERRANEAN AREAGiovanni Grillone, Giorgio Baiamonte, Francesco D’AsaroRunoff estimation in ungauged basin is a challenge for the hydrological engineers and planners. For any hydrological study on an ungauged basin, a methodology has to be appropriately selected for the determination of runoff at its outlet. Several methods have been used to estimate the basin runoff production. In this study the empirical Kennessey method to determine average annual runoff … …American Journal of Applied Sciences, Vol. 11, Issue 1 · 5 Dec 2013 · 8,218 accessesView PDFCite
Research ArticleOpen accessIn the abstractESTIMATION OF CLAIM COST DATA USING ZERO ADJUSTED GAMMA AND INVERSE GAUSSIAN REGRESSION MODELSYulia Resti, Noriszura Ismail, Saiful Hafizah JamaanIn actuarial and insurance literatures, several researchers suggested generalized linear regression models (GLM) for modeling claim costs as a function of risk factors. The modeling of claim costs involving both zero and positive claims experience has been carried out by fitting the claim costs collectively using Tweedie model. However, the probability of zero claims in Tweedie model is not allowed … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 11 Jul 2013 · 7,263 accessesView PDFCite