On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

Korkmaz, Mustafa Ç.; Chesneau, Christophe; Korkmaz, Zehra Sedef

doi:10.3390/sym13010117

Cited by 38 publications

(29 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other similar quantile regressions and unit models recently proposed can be found in Gündüz and Korkmaz ( 2020 ), Korkmaz ( 2020a , 2020b ), Korkmaz et al. ( 2021 ).…”

Section: Introductionsupporting

confidence: 57%

A new quantile regression for the COVID-19 mortality rates in the United States

et al. 2021

View full text Add to dashboard Cite

An outbreak of coronavirus disease 2019 (COVID-19) has quickly spread worldwide from December 2019, thus characterizing a pandemic. Until August 2020, the United States of America (U.S.) accounted for almost one-fourth of the total deaths by coronavirus. In this paper, a new regression is constructed to identify the variables that affected the first-wave COVID-19 mortality rates in the U.S. states. The mortality rates in these states are computed by considering the total of deaths recorded on 30, 90, and 180 days from the 10th recorded case. The proposed regression is compared to the Kumaraswamy and unit-Weibull regressions, which are useful in modeling proportional data. It provides the best goodness-of-fit measures for the mortality rates and explains of its variability. The population density, Gini coefficient, hospital beds, and smoking rate explain the median of the COVID-19 mortality rates in these states. We believe that this article’s results reveal important points to face pandemic threats by the State Health Departments in the U.S.

show abstract

“…Other similar quantile regressions and unit models recently proposed can be found in Gündüz and Korkmaz ( 2020 ), Korkmaz ( 2020a , 2020b ), Korkmaz et al. ( 2021 ).…”

Section: Introductionsupporting

confidence: 57%

A new quantile regression for the COVID-19 mortality rates in the United States

et al. 2021

View full text Add to dashboard Cite

show abstract

“…[25] introduced the Kumaraswamy quantile regression model for the dependent variables on the (0, 1) interval. The presented idea in the study of [25] has been applied to different probability distributions by [9,11,12,14,[26][27][28][29].…”

Section: The New Quantile Regression Model Based On the Qlep Distribution For The Unit Responsementioning

confidence: 99%

“…Similarly, [8] used X = T/(T + 1) transformation on the improved second-degree Lindley (ISDL) distribution and resulting distribution was called as unit-ISDL distribution. These approaches have been used many authors such as [9][10][11][12][13][14][15][16][17][18][19] and so on.…”

Section: Introductionmentioning

confidence: 99%

The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model

et al. 2021

Self Cite

View full text Add to dashboard Cite

Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum likelihood method. In addition, a new regression model is defined under the proposed distribution and its residual analysis is examined. As a result of the empirical studies on real data sets, it is observed that the proposed regression model gives better results than the unit-Weibull and Kumaraswamy regression models.

show abstract

“…e median is the value in the sorted sample that falls in the middle (middle quantile, q 50 th � 0.50). Interested readers may refer to studies by Koenker and Bassett [31], Koenker and Hallock [32], Koenker [33], Hao et al [34], and Korkmaz and Chesneau [35].…”

Section: Proposed Class Of Quantile Regression-ratio-type Estimatorsmentioning

confidence: 99%

Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

Shahzad

Al‐Noor

Afshan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.

show abstract

On the Arcsecant Hyperbolic Normal Distribution. Properties, Quantile Regression Modeling and Applications

Cited by 38 publications

References 35 publications

A new quantile regression for the COVID-19 mortality rates in the United States

A new quantile regression for the COVID-19 mortality rates in the United States

The Log Exponential-Power Distribution: Properties, Estimations and Quantile Regression Model

Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

Contact Info

Product

Resources

About