Research ArticleOpen accessPhrase in the titleSparse Sliced Inverse Quantile RegressionAli Alkenani, Tahir R. DikheelThe current paper proposes the sliced inverse quantile regression method (SIQR). In addition to the latter this study proposes both the sparse sliced inverse quantile regression method with Lasso (LSIQR) and Adaptive Lasso (ALSIQR) penalties. This article introduces a comprehensive study of SIQR and sparse SIQR. The simulation and real data analysis have been employed to check the performance of … …Journal of Mathematics and Statistics, Vol. 12, Issue 3 · 10 Sep 2016 · 7,131 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAnalyzing the Role of Computerized Accounting Usefulness with Quantile RegressionQuang Linh HuynhPrevious research has examined the determinants resulting in the implementation of computerized accounting, mainly relying on the Generalized Linear Model (GLM), based on the conditional mean of dependent variables. The aim of this document is to analyze the implementation of computerized accounting systems with the quantile regression. Additionally, it attempts to study the mediating role of the perceived usefulness of … …American Journal of Applied Sciences, Vol. 12, Issue 9 · 14 Oct 2015 · 6,580 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleAN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSIONYanan Hu, Qianqian Zhu, Maozai TianIn this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 4 Jan 2014 · 6,813 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleQuantile Regression Estimation Using Non-Crossing ConstraintsIlaria Lucrezia AmeriseIn this article we are concerned with a collection of multiple linear regressions that enable the researcher to gain an impression of the entire conditional distribution of a response variable given a set of explanatory variables. More specifically, we investigate the advantage of using a new method to estimate a bunch of non-crossing quantile regressions hyperplanes. The main tool is … …Journal of Mathematics and Statistics, Vol. 14, Issue 1 · 15 May 2018 · 7,700 accessesView PDFCite
Research ArticleOpen accessPhrase in the titleDrinking Wine at Home: Hedonic Analysis of Sicilian Wines Using Quantile RegressionGiuseppe Di Vita, Francesco Caracciolo, Luigi Cembalo et al.In recent decades, the Sicilian wine industry has experienced a booming expansion because of the growing preferences of Italian consumers for Sicilian wines, especially in extra-regional markets. These consumers have been paying closer attention to Sicilian premium wines. For this reason, the objective of this study is to inform professional investors and wine managers about the consumer preferences with respect … …American Journal of Applied Sciences, Vol. 12, Issue 10 · 14 Oct 2015 · 7,210 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA NOVEL ROBUST AND EFFICIENT TOOL FOR DETECTING HETEROSCEDASTICITYWei Xiong, Maozai TianOne of the greatest values of Quantile Regression (QR) is that it provides a good procedure in the sense that QR could be much more efficient and sometimes arbitrarily more efficient in recovering the mean function than the Least Squares (LS) even when without moment conditions. However, heteroscedasticity definitely causes conditional variances of parametric or nonparametric estimates of mean functions … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 21 Mar 2014 · 6,657 accessesView PDFCite
Research ArticleOpen accessIn the keywordsPrediction of the Daily Mean PM10 Concentrations Using Linear ModelsJ. C.M. Pires, F. G. Martins, S. I.V. Sousa et al.The performance of five linear models to predict the daily mean PM10 concentrations was compared. The linear models proposed were: (i) multiple linear regression; (ii) principal component regression; (iii) independent component regression; (iv) quantile regression; and (v) partial least squares regression. The study was based on data from an urban site in Oporto Metropolitan Area and the analysed period was … …American Journal of Environmental Sciences, Vol. 4, Issue 5 · 31 Oct 2008 · 7,132 accessesView PDFCite