A new flexible regression model with application to recovery probability Covid-19 patients

Prataviera, Fábio; Hashimoto, Elizabeth M.; Ortega, Edwin M. M.; Cordeiro, Gauss M.; Cancho, Vicente G.; Vila, Roberto

doi:10.1080/02664763.2022.2163229

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…[7] proposed a parametric and a partially linear regression model called genralized odd log-logistic Birnbaum-Saunders distribution, and ref. [8] defined the generalized odd log-logistic Maxwell mixture model to analyze COVID-19 Chinese data.…”

Section: Introductionmentioning

confidence: 99%

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

da Costa,

de Lima,

Cordeiro

2023

COVID

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This work aims to study the factors that explain the COVID-19 vaccination rate through a generalized odd log-logistic Lindley regression model with a shape systematic component. To accomplish this, a dataset of the vaccination rate of 254 counties in the state of Texas, US, was used, and simulations were performed to investigate the accuracy of the maximum likelihood estimators in the proposed regression model. The mathematical properties investigated provide important information about the characteristics of the distribution. Diagnostic analysis and deviance residuals are addressed to examine the fit of the model. The proposed model shows effectiveness in identifying the key variables of COVID-19 vaccination rates at the county level, which can contribute to improving vaccination campaigns. Moreover, the findings corroborate with prior studies, and the new distribution is a suitable alternative model for future works on different datasets.

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Section: Introductionmentioning

confidence: 99%

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

da Costa,

de Lima,

Cordeiro

2023

COVID

Self Cite

View full text Add to dashboard Cite

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The generalized odd log-logistic-G regression with interval-censored survival data

Vigas

Ortega

Suzuki

et al. 2023

Journal of Applied Statistics

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GOLL_W<-function(tempo,alpha,theta,a,b){ gt5=(a*tempo^(a-1)/b^a)*(exp(-(tempo/b)^a)) Gt5=1-exp(-(tempo/b)^a) ft5=(alpha*theta*gt5*(Gt5^(alpha*theta-1))*((1-Gt5^(theta))^(alpha-1)))/((Gt5^(alpha*theta)+(1-Gt5^theta)^alpha)^2) Ft5=(Gt5^(alpha*theta))/(Gt5^(alpha*theta)+ (1-Gt5^(theta))^alpha) St5=1-Ft5 ht5=ft5/St5 return(ht5) } tempo=c(seq(0,10,0.01)) v_lty <-c('longdash ', 'dotdash', 'dotted', 'dashed', 'solid')

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A new flexible regression model with application to recovery probability Covid-19 patients

Cited by 2 publications

References 26 publications

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

Analyzing County-Level COVID-19 Vaccination Rates in Texas: A New Lindley Regression Model

The generalized odd log-logistic-G regression with interval-censored survival data

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