Inflated Kumaraswamy distributions

Cribari‐Neto, Francisco; Santos, João

doi:10.1590/0001-3765201920180955

Cited by 13 publications

(7 citation statements)

References 9 publications

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“…To model such kind of data, we look for a mixture of distributions. Ospina and Ferrari ( 9 ) and Cribari-Neto and Santos ( 10 ) introduced inflated Beta distributions and inflated Kumaraswamy distributions which is a mixture of discrete and continuous distributions. Ospina and Ferrari ( 11 ) and Bayer et al ( 12 ) introduced the inflated Beta regression model and inflated Kumaraswamy regression model.…”

Section: Introductionmentioning

confidence: 99%

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

Lobo

Kamath

Eshwara

2023

Front. Public Health

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BackgroundAntimicrobial resistance has emerged as one of the foremost public health troubles of the 21st century. This has ended in a public health disaster of the global situation, which threatens the exercise of present-day remedy. There is an urgent requirement for a cost-effective strategy to reduce antimicrobial resistance. Infectious disease control researchers most often analyze and predict antimicrobial resistance rate data that includes zeros or ones. Commonly used time-series analysis such as autoregressive moving average model is inappropriate for such data and may arrive at biased results.ObjectiveThis study aims to propose a time-series model for continuous rates or proportions when the interval of series includes zeros or ones and compares the model with existing models.DataThe Escherichia coli, isolated from blood cultures showing variable susceptibility results to different antimicrobial agents, has been obtained from a clinical microbiology laboratory of a tertiary care hospital, Udupi district, Karnataka, during the years between 2011 and 2019.MethodologyWe proposed a Degenerate Beta Autoregressive model which is a mixture of continuous and discrete distributions with probability mass at zero or one. The proposed model includes autoregressive terms along with explanatory variables. The estimation is done using maximum likelihood with a non-linear optimization algorithm. An R shiny app has been provided for the same.ResultsThe proposed Degenerate Beta Autoregressive model performed well compared to the existing autoregressive moving average models. The forecasted antimicrobial resistance rate has been obtained for the next 6 months.ConclusionThe findings of this article could be beneficial to the infectious disease researchers to use an appropriate time-series model to forecast the resistance rate for the future and to have better or advance public health policies to control the rise in resistance rate.

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Section: Introductionmentioning

confidence: 99%

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

Lobo

Kamath

Eshwara

2023

Front. Public Health

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“…If

c = 0

, the density (3) is called zero‐inflated UW distribution, and if

c = 1

the density (3) is called one‐inflated UW distribution. It should be mentioned that this approach for construction of inflated parametric distributions limited in the unit interval was considered in Ospina and Ferrari (2008) and Cribari‐Neto and Santos (2019).…”

Section: Zero‐or‐one Inflated Unit‐weibull Quantile Regression Modelsmentioning

confidence: 99%

“…In this context, for independent data, Ospina and Ferrari (2008) proposed inflated beta distributions as natural alternatives to the beta distributions for modeling data observed in [0, 1), (0, 1], or [0, 1]. Recently, Cribari‐Neto and Santos (2019) established the inflated Kumaraswamy distributions. In order to accommodate explanatory variables in the modeling, Hoff (2007) introduced the one‐inflated beta model.…”

Section: Introductionmentioning

confidence: 99%

A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

2021

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Over the last decades, the challenges in applied regression have been changing considerably, and full probabilistic modeling rather than predicting just means is crucial in many applications. Motivated by two applications where the response variable is observed on the unit‐interval and inflated at zero or one, we propose a parametric quantile regression considering the unit‐Weibull distribution. In particular, we are interested in quantifying the influence of covariates on the quantiles of the response variable. The maximum likelihood method is used for parameters estimation. Monte Carlo simulations reveal that the maximum likelihood estimators are nearly unbiased and consistent. Also, we define a residual analysis to assess the goodness of fit.

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“…The derived model uses a continuous distribution on (0, ∞) and a degenerate distribution that assigns non-negative probability to zerothe Bernoulli distribution-for observed values equal to zero. The same approach has been applied in the beta and Kumaraswamy distributions [10,11]. To demonstrate the applicability of the proposed model, an experiment considering a SAR image from the ICEYE radar is conducted, showing the promising performance of the derived distribution in modeling three different regions with different noise and clutter characteristics.…”

Section: Introductionmentioning

confidence: 99%

Inflated Rayleigh Distribution for SAR Imagery Modeling

Palm

Javadi

Bayer

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Synthetic aperture radars (SAR) data plays an important role in remote sensing applications. It is common knowledge that SAR image amplitude pixels can be approximately modeled by the Rayleigh distribution. However, this model is continuous and does not accommodate points with non-zero probability, such as a null pixel amplitude value. Thus, in this paper, we propose an infated Rayleigh distribution for SAR image modeling that is based on a mixed continuous-discrete distribution and can be used to ft signals with observed values on [0, ∞). The maximum likelihood approach is considered to estimate the parameters of the proposed distribution. An empirical experiment with a SAR image is also presented and discussed.

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Inflated Kumaraswamy distributions

Cited by 13 publications

References 9 publications

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app

A parametric quantile regression approach for modelling zero‐or‐one inflated double bounded data

Inflated Rayleigh Distribution for SAR Imagery Modeling

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