Article
Mathematics
Rashad A. R. Bantan, Christophe Chesneau, Farrukh Jamal, Mohammed Elgarhy, Waleed Almutiry, Amani Abdullah Alahmadi
Summary: The article presents the modified Kumaraswamy distribution, which combines logarithmic, power, and ratio functions, demonstrating strong flexibility and excellent statistical properties. The new model outperforms other common distribution models in data fitting and practical applications.
Article
Mathematics
Weizhong Tian, Liyuan Pang, Chengliang Tian, Wei Ning
Summary: In this paper, the change point of the Kumaraswamy distribution is analyzed using the methods of likelihood ratio test, modified information criterion, and Schwarz information criterion. Simulation experiments are conducted to evaluate the performance of these three methods. The feasibility of the proposed method is illustrated by applying it to a real dataset in the application section.
Article
Multidisciplinary Sciences
Osama E. Abo-Kasem, Ahmed R. El Saeed, Amira I. El Sayed
Summary: In this paper, the non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring is studied. The maximum likelihood estimates, maximum product spacings, and asymptotic distribution of the parameters are derived. Bayesian estimators under symmetric and asymmetric loss functions are obtained using the Lindley approximation and Markov chain Monte Carlo method. The performance of the point and interval estimators is evaluated through simulation studies.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Amal S. Hassan, Ehab M. Almetwally, Gamal M. Ibrahim
Summary: This paper introduces a new statistical distribution for analyzing the mortality rate of COVID-19, and its application in different countries shows that this distribution is more suitable than other competitive models.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Physics, Fluids & Plasmas
Takashi Arai
Summary: The proposed probability distribution for multivariate binary random variables is represented by principal minors of the parameter matrix, similar to the inverse covariance matrix in the multivariate Gaussian distribution. The model allows for analytical expressions of the partition function, central moments, and marginal and conditional distributions. Additionally, the proposed distribution can be obtained using Grassmann numbers, with the inverse matrix representing partial correlation.
Article
Mathematics, Applied
Qasim Ramzan, Muhammad Amin, Ahmed Elhassanein, Muhammad Ikram
Summary: This paper introduces a new six-parameter extension of the Weibull distribution, known as the EGIKw-Weibull, commonly used for modeling lifetime data. Various useful properties of the new distribution are derived and investigated using Monte Carlo simulation to study maximum likelihood estimator (MLE). Two real applications are presented in the paper.
Article
Mathematics
Guillermo Martinez-Florez, Roger Tovar-Falon
Summary: This paper introduces two new distributions for modeling data, suitable for data with positive support and data on the (0,1) interval. The extensions to regression models were studied, and statistical inference was conducted using the maximum likelihood method. A small simulation study and applications to real data sets were used to evaluate the effectiveness of the methodology.
Article
Multidisciplinary Sciences
Yuri A. Iriarte, Mario de Castro, Hector W. Gomez
Summary: The Lambert-uniform distribution is proposed as a new one-parameter distribution for modeling bounded data, showing favorable behavior in certain scenarios and being considered as an alternative to well-known one-parameter and two-parameter distributions in statistical literature. Parameter estimation is done using maximum likelihood method and evaluated through simulation experiments.
Article
Multidisciplinary Sciences
Aisha Fayomi, Amal S. Hassan, Ehab M. Almetwally
Summary: A new probability distribution called the unit-exponentiated Lomax (UEL) distribution is proposed for modeling data on the unit interval. The study estimates the parameters of the UEL distribution using Bayesian, maximum product of spacing, and maximum likelihood estimation techniques, and evaluates the performance of various estimators through simulated scenarios. The UEL regression model is demonstrated as an alternative to unit-Weibull regression, beta regression, and linear regression models using mock jurors, food spending, and Covid-19 data, showing superior performance compared to certain other unit distributions.
Article
Multidisciplinary Sciences
Liang Wang, Ying Zhou, Yuhlong Lio, Yogesh Mani Tripathi
Summary: This paper discusses generalized progressive hybrid censoring and designs a scheme for collecting failure information throughout the entire life cycle of units. It investigates inference methods for units following Kumaraswamy distribution, including maximum likelihood estimates and Bayesian estimates. Simulation studies and a real-life example are presented for illustration purposes.
Article
Statistics & Probability
Jimmy Reyes, Jaime Arrue, Osvaldo Venegas, Hector W. Gomez
Summary: This work introduces an extension of the Lindley-Weibull distribution, which is obtained by using the quotient of two independent random variables. The new distribution's pdf, cdf, and risk functions are presented and its statistical properties, moments, skewness, and kurtosis are studied. The parameter estimation is done using the maximum likelihood method and assessed through a Monte Carlo simulation study, with nutrition data used as an example to illustrate the proposed model's utility.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Statistics & Probability
Mustafa C. Korkmaz, Zehra Sedef Korkmaz
Summary: A new distribution, called the unit log-log distribution, is proposed in this paper, and its basic properties as well as parameter estimation methods are studied. Through applications on real data sets and Monte Carlo simulation studies, it is demonstrated that the proposed models have better modeling abilities.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Mathematics
Yongxia Zhang, Qi Wang, Maozai Tian
Summary: This paper investigates variable selection for a dataset with heavy-tailed distribution and high correlations within blocks of covariates. By introducing a latent factor model and a consistency strategy named Farvsqr, the study successfully addresses the challenges of high-dimensional data and highly correlated covariates.
Article
Mathematics
Saieed F. Ateya, Randa Alharbi, Mutua Kilai, Ramy Aldallal
Summary: This paper constructs a new unified progressive hybrid censoring scheme UPHCS that covers eleven famous censoring schemes. It studies the estimation problem of Burr-X distribution parameters using maximum likelihood and Bayes approaches based on the suggested unified progressive hybrid censored samples. Two real data sets are used as illustrative engineering examples.
JOURNAL OF MATHEMATICS
(2022)
Article
Engineering, Multidisciplinary
Fuad S. Alduais, Mansour F. Yassen, Mohammed M. A. Almazah, Zahid Khan
Summary: This paper presents the Bayesian estimation of parameters in the Kumaraswamy distribution (KD) using type-II censoring data. Several loss functions are introduced, and the gamma distribution is utilized as a conjugate distribution in the Bayesian framework. A new loss function called the weighted composite loss function (WCLLF) is established, and it outperforms other methods in determining the shape parameter of the KD, as shown in the Monte Carlo simulation.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Environmental Sciences
Jorge Alberto Achcar, Ricardo Puziol de Oliveira
Summary: In this study, non-homogeneous Poisson processes (NHPP) were used to analyze climate data by assuming different parametric forms for the intensity function. The models considered the numbers of years that a climate variable measurement exceeded a given threshold, and employed a Bayesian approach to estimate parameters. The models were applied to climate data from Kazakhstan, Uzbekistan, and the USA.
ENVIRONMENTAL MODELING & ASSESSMENT
(2022)
Article
Environmental Sciences
Danrley de Abreu dos Santos, Andrey Cassiano Martins, Kauana Mara da Silva, Amanda Correa Nunes, Yara Campos Miranda, Andre Aguiar Battistelli, Ricardo Puziol de Oliveira, Rodrigo Camilo, Jorge Alberto Achcar
Summary: The study examined the relationship between ammonia nitrogen concentrations in the Columbia River and factors such as dissolved oxygen, pH, specific conductivity, and temperature. The results suggest that these factors could potentially affect ammonia nitrogen concentrations, which may help authorities in implementing strategies to control water pollution.
RIVER RESEARCH AND APPLICATIONS
(2022)
Article
Statistics & Probability
Marcos Vinicius de Oliveira Peres, Jorge Alberto Achcar, Ricardo Puziol de Oliveira, Edson Zangiacomi Martinez
Summary: The study proposed a bivariate model based on a defective Gompertz distribution and Clayton copula function to capture the dependence structure between lifetimes. Extensive simulation study evaluated biases and mean squared errors of maximum likelihood estimators, showing the usefulness of the model in medical data applications. Maximum likelihood and Bayesian methods were used to estimate model parameters.
AUSTRIAN JOURNAL OF STATISTICS
(2022)
Article
Mathematics
Josmar Mazucheli, Bruna Alves, Mustafa C. Korkmaz, Victor Leiva
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
Statistics & Probability
Andre F. B. Menezes, Marcelo Bourguignon, Josmar Mazucheli
Summary: This paper proposes a novel regression model for bounded data as an alternative to the commonly used beta regression model. The new model, which utilizes maximum likelihood estimation, is shown to be a strong competitor and is evaluated through Monte Carlo experiments and real applications.
JOURNAL OF STATISTICAL THEORY AND PRACTICE
(2022)
Article
Computer Science, Artificial Intelligence
Josmar Mazucheli, Mustafa C. Korkmaz, Andre F. B. Menezes, Victor Leiva
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
Statistics & Probability
Emerson Barili, Jorge Alberto Achcar, Ricardo Puziol de Oliveira
Summary: This study aims to analyze long-term rainfall data in five Central Asian countries to understand the behavior of monthly rainfall and its possible link with climate change. The results indicate that certain factors have varying effects on monthly rainfall in these countries, potentially related to global climate change.
PAKISTAN JOURNAL OF STATISTICS AND OPERATION RESEARCH
(2022)
Article
Dentistry, Oral Surgery & Medicine
Jeniffer Perussolo, Flavia Matarazzo, Debora R. Dias, Ricardo P. Oliveira, Mauricio G. Araujo
Summary: Brushing discomfort around dental implants with inadequate keratinized mucosa may indicate peri-implant diseases, but does not affect tissue inflammation.
CLINICAL ORAL IMPLANTS RESEARCH
(2022)
Article
Health Care Sciences & Services
Ricardo Puziol de Oliveira, Marcos Vinicius de Oliveira Peres, Edson Z. Martinez, Jorge Alberto Achcar
Summary: The present study introduces a new multivariate mixture cure rate model for modeling recurrent event data in the presence of cure fraction. The study provides an alternative approach for analyzing bivariate lifetime data with covariates, censored data, and cure fraction using a new bivariate parametric model.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Statistics & Probability
Ricardo Puziol de Oliveira, Marcos Vinicius de Oliveira Peres, Jorge Alberto Achcar, Edson Z. Martinez
Summary: The present study introduces a new bivariate distribution based on the Sushila distribution to model bivariate lifetime data. The new distribution takes into consideration the presence of a cure fraction, right-censored data, and covariates. The study demonstrates the methodology of obtaining the new bivariate probability distribution and the introduction of the cure rate using a generalization of standard mixture models.
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Victor Leiva, Josmar Mazucheli, Bruna Alves
Summary: This study discusses the frequently occurring covariate-related response variables in diverse studies. Instead of relying on the mean, fractile regression is used to model this relationship. A novel quantile regression model based on a parametric distribution is formulated, and an R package is used for estimation and model checking. The model is applied to case studies using COVID-19 and medical data from Brazil and the United States.
FRACTAL AND FRACTIONAL
(2023)
Article
Dentistry, Oral Surgery & Medicine
Maysa Koster, Debora R. Dias, Gabriela de S. Zimiani, Rafael P. da M. Santos, Ricardo P. de Oliveira, Mauricio G. Araujo, Roberto M. Hayacibara
Summary: This retrospective case series aimed to assess the stability of the soft tissue and marginal bone around four single crowns supported by narrow-diameter implants replacing all maxillary incisors. The results showed that the papilla height between implants and between tooth and implant increased significantly over time, while the marginal bone level remained stable. Patients were highly satisfied with the treatment outcome.
CLINICAL ORAL IMPLANTS RESEARCH
(2023)
Article
Statistics & Probability
Danielle Peralta, Ricardo Puziol de Oliveira, Jorge Alberto Achcar
Summary: This paper presents a novel approach for analyzing bivariate positive data with left-censored observations and covariate information using hierarchical Bayesian analysis. The proposed method assumes marginal Weibull distributions and employs either a usual Weibull likelihood or Weibull-Tobit likelihood approaches. The results demonstrate that incorporating a latent factor in the bivariate model to capture potential dependence produces accurate inference results, and the proposed hierarchical Bayesian analysis is promising for analyzing such data.
JOURNAL OF APPLIED STATISTICS
(2023)
Article
Social Sciences, Interdisciplinary
Ricardo Puziol de Oliveira, Jorge Alberto Achcar, Wesley Bertoli, Josmar Mazucheli, Yara Campos Miranda
Summary: This paper presents a comprehensive study on the COVID-19 epidemic in Brazil using counting data. A nonlinear rational polynomial function is utilized to model the reported cases and deaths, and the least squares method is applied to fit the model. The findings suggest that the number of cases and deaths is still increasing without any evidence of reaching a peak or decreasing trend. The results demonstrate the model's ability to accurately predict the growth curve of COVID-19 in Brazil.
REVISTA TECNOLOGIA E SOCIEDADE
(2022)
