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Article
Statistics & Probability
Mustafa C. Korkmaz et al.
Summary: This article introduces a new distribution with two tuning parameters specified on the unit interval, obtained through a 'hyperbolic secant transformation' of a random variable following the Weibull distribution. The lack of research on hyperbolic transformations providing flexible distributions over the unit interval motivated this study. The main distributional structural properties of the new distribution are established, and different estimation methods and two simulation works are derived for model parameters. Additionally, a related quantile regression model is developed for further statistical perspectives, and real data applications are considered to evaluate the model's fitting power.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Engineering, Multidisciplinary
Victor Leiva et al.
Summary: This work proposes a methodology for monitoring a shift in the quantile of a distribution belonging to the log-symmetric family. The parametric bootstrap method is used to determine the sampling distribution and establish control limits. Monte Carlo simulations are conducted to assess the performance of the proposed bootstrap control charts. An application in the field of reliability data is presented. The research also provides an R package named chartslogsym for public use.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Mathematics
Helton Saulo et al.
Summary: Income modeling is crucial in determining workers' earnings and is an important research topic in labor economics. Traditional regressions based on normal distributions are widely used but not suitable for asymmetric income data. This study proposes parametric quantile regressions based on two asymmetric income distributions: Dagum and Singh-Maddala. Monte Carlo simulation studies and empirical data analysis show that both models perform well in model fitting for positively asymmetrically distributed income data. The economic implications of this investigation are discussed, and the proposed models are valuable tools for statisticians and econometricians.
Article
Computer Science, Artificial Intelligence
Josmar Mazucheli et al.
Summary: This paper proposes and derives a new regression model for response variables defined on the open unit interval. By reparameterizing a distribution, the interpretation of its location parameter is obtained. The effects of explanatory variables in the conditional quantiles of the response variable are evaluated as an alternative method. The suitability of the proposal is demonstrated through simulations and real applications.
Article
Mathematics
Harry Haupt et al.
Summary: This study focuses on the identification and estimation of trends in hydroclimatic time series. It provides an asymptotic justification for quantile trend regression modeling and explores its application in analyzing temperature anomalies. The results highlight the presence of heterogenous trends and an increase in the relative frequency of unusually high temperatures.
Article
Mathematics
Kyulee Shin et al.
Summary: This study uses quantile regression methods to deepen our understanding of the prediction and structural relationship between a student's academic performance and regular after-school exercise. The study finds that negative relationships dominate in the prediction models, while the relationships are reversed in the structural models. Furthermore, the study also reveals that low-performing students have stronger associations between the two variables compared to high-performing students.
Article
Statistics & Probability
Helton Saulo et al.
Summary: This article introduces a regression model based on the log-symmetric family of distributions, which is useful for dealing with continuous, positive, and asymmetrically distributed response variables. Through two Monte Carlo simulation studies, it was found that the maximum likelihood estimators perform well.
STATISTICA NEERLANDICA
(2022)
Article
Computer Science, Interdisciplinary Applications
Josmar Mazucheli et al.
Summary: Quantile regression allows estimation of the relationship between covariates and any quantile of the response variable. This study provides a new computational package, two biomedical applications (one using COVID-19 data), and an up-to-date overview of parametric quantile regression.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Mathematics
Josmar Mazucheli et al.
Summary: The Vasicek distribution is a two-parameter probability model that plays an important role in statistical applications, particularly in finance. This paper proposes two Vasicek regression models for analyzing data on the unit interval, one using a quantile parameterization and the other using the original parameterization. Monte Carlo simulations are conducted to evaluate the statistical properties of the estimators, and an R package is developed to provide the results of the investigation. Applications with real data sets demonstrate the practical usage of the Vasicek quantile and mean regressions as alternatives to other well-known models.
Article
Mathematics
Mustafa C. Korkmaz et al.
Summary: In this paper, a new distribution defined on (0, 1) is introduced and its basic distributional properties are studied. Different methods of estimation for related parameters are examined, and their performance is assessed through a complete simulation study. Furthermore, a quantile regression model based on the proposed distribution is introduced, and its superior modeling capabilities are demonstrated through applications to real data sets.
MATHEMATICA SLOVACA
(2022)
Article
Mathematics, Interdisciplinary Applications
Shuangshuang Li et al.
Summary: This paper focuses on a random effects semiparametric regression model (RESPRM) with separable space-time filters, constructing profile quasi-maximum likelihood estimators for parameters and nonparametric functions and a generalized F-test statistic for checking nonlinear relationships. The asymptotic properties of estimators and distribution of test statistic are derived, showing good finite sample performance through Monte Carlo simulations on Indonesian rice farming data.
FRACTAL AND FRACTIONAL
(2022)
Article
Operations Research & Management Science
Luis Sanchez et al.
Summary: The study introduces a class of quantile regression models based on the Birnbaum-Saunders distribution, providing wide flexibility in modeling positive and asymmetric data. The methodology includes thorough theoretical property study and diagnostic analytics evaluation, with numerical results indicating the adequacy of the approach for quantile regression. The BS distribution is shown to be a good modeling choice for data with positive support and asymmetry.
APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY
(2021)
Article
Mathematics, Applied
Mustafa C. Korkmaz et al.
Summary: This paper introduces a new unit Burr-XII distribution and studies its basic statistical properties. Different estimation methods are evaluated through two simulation studies, and a new quantile regression model based on the proposed distribution is introduced. Applications to real data sets show that the proposed models have better modeling capabilities than competing models.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Multidisciplinary Sciences
Mustafa C. Korkmaz et al.
Summary: This paper introduces a new distribution defined on the unit interval, and examines its statistical properties and parametric estimation. The proposed distribution shows competitiveness in practical applications based on simulation studies and applications to real datasets.
Article
Mathematics, Applied
Tatiane Fontana Ribeiro et al.
Summary: This paper constructs a new regression model to analyze the COVID-19 mortality rates in U.S. states, showing that factors such as population density, income Gini coefficient, hospital beds, and smoking rate are influential. The results provide important insights for State Health Departments in facing pandemic threats.
COMPUTATIONAL & APPLIED MATHEMATICS
(2021)
Article
Mathematics
Luis Sanchez et al.
Summary: Quantile regression is a better alternative for describing asymmetrically distributed data, especially when the response follows an asymmetrical distribution. This study proposes a new approach to quantile regression based on the Weibull distribution and discusses its practical application. The method presented in this work allows for a better understanding of the central tendency of the data.
Article
Multidisciplinary Sciences
Josmar Mazucheli et al.
Summary: This work introduces a regression model based on the unit Birnbaum-Saunders distribution for continuous variables bounded to the unit interval, offering better performance compared to existing quantile regression models.
Article
Mathematics, Interdisciplinary Applications
Ahmed I. Shahin et al.
Summary: This paper introduces a deep learning time-series prediction model to forecast COVID-19 confirmed, recovered, and death cases. The proposed model demonstrates high accuracy in experiments, outperforming other forecasting models with lower error values and a higher R-squared value of 0.99.
FRACTAL AND FRACTIONAL
(2021)
Article
Statistics & Probability
Christian E. Galarza et al.
Summary: This paper proposes a robust logistic quantile regression model using a logit link function and the EM-based algorithm for maximum likelihood estimation, which performs better in handling asymmetric probability distributions of observed responses.
SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS
(2021)
Article
Mathematics
Luis Sanchez et al.
Article
Statistics & Probability
Helton Saulo et al.
STATISTICAL PAPERS
(2019)
Article
Automation & Control Systems
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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
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Article
Engineering, Environmental
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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2019)
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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
(2018)
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Mathematical & Computational Biology
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STATISTICS IN MEDICINE
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JOURNAL OF STATISTICAL SOFTWARE
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Journal of Statistical Software
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Journal of Statistical Software
(2015)
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Journal of Statistical Software
(2015)
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Computer Science, Interdisciplinary Applications
Victor Leiva et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2014)
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Pablo A. Mitnik et al.
STATISTICAL PAPERS
(2013)
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Samuel Kotz et al.
METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
(2010)
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Samuel Kotz et al.
METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
(2010)
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SLP Ferrari et al.
JOURNAL OF APPLIED STATISTICS
(2004)
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KM Yu et al.
STATISTICS & PROBABILITY LETTERS
(2001)
