Search
5 articles for “Quantile regression”
-
Scaled Box-Cox Quantile Regression Using the Geometric Mean as The Scaling Factor
Abstract: Box-Cox transformation since its proposal has been modified by many authors. It was extended to quantile regression as Box-Cox quantile regression. An option of Box-Cox transformation that doesn’t appear to have been explored much in literature is the scaled Box-Cox transformation. This study extends the Box-Cox quantile regression to accommodate scaling of the response variable using the geometric mean as the scaling factor. A simulation study demonstrates that the scaled …
Published in Research & Reviews : Journal of Statistics · Vol. 10, Issue 3, 2021 · pp. 29–33 Read article
-
Parametric Quantile Regression using Quantile-based Asymmetric Family of the Distributions
Abstract: Quantile regression is used for finding the effects of covariates on different quantiles of the response variable. The contribution of this article is to develop a theory of quantile regression using the Quantile-based asymmetric (QBA) family (similar to GAMLSS for mean regression) and then apply the proposed approach for analyzing COVID-19 data. More precisely, we estimate regression quantiles and investigate the asymptotic estimators under a generalized linear modeling framework, but …
Published in Research & Reviews : Journal of Statistics · Vol. 11, Issue 3, 2022 · pp. 54–66 Read article
-
Comparison between Generalized Least Square Method and Quantile Regression in Dealing with Heteroscedastic Data
Abstract: Heteroscedastic data arise from a population whose sub-populations have different variabilities from one another. In presence of heteroscedasticity, the OLS estimators no longer remain the BLUEs, giving rise to wider acceptance regions and confidence intervals for the model parameters. The generalized least square technique, specifically weighted least square method is the conventional way of eliminating heteroscedasticity. However, quantile regression can also be used in dealing with this kind of data. …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 12–16 Read article
-
Regression Analysis of Research Data using R Programming
Abstract: ABSTRACTThe goal of the review paper is discussing the relationship between various types of regression analysis whose output was sufficiently analyzed using R programming. The primary purpose of this paper is to explain the relationship of linear, multiple, quantile and polynomial regression models to achieve final conclusion with different data sets. Therefore, this paper presents the easiest way of regression analysis commands and R programming strengths for of data analysis. …
Published in Recent Trends in Programming languages · Vol. 6, Issue 2, 2019 · pp. 42–50 Read article
-
Heteroscedasticity in Survey Data and Model Selection based on Weighted Schwarz-Bayesian Information Criterion
Abstract: AbstractThis paper proposed Weighted Schwarz Bayesian Information criterion for the purpose of selecting a best model from various competing models, when heteroscedasticity is present in the survey data. The authors found that the information loss between the true model and fitted models are equally weighted, instead of giving unequal weights. The computation of weights purely depends on the differential entropy of each sample observation and traditional Schwarz Bayesian information criterion …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 3, 2013 · pp. 8–23 Read article