RESUMEN
The aim of this study is to propose a generalized odd log-logistic Maxwell mixture model to analyze the effect of gender and age groups on lifetimes and on the recovery probabilities of Chinese individuals with COVID-19. We add new properties of the generalized Maxwell model. The coefficients of the regression and the recovered fraction are estimated by maximum likelihood and Bayesian methods. Further, some simulation studies are done to compare the regressions for different scenarios. Model-checking techniques based on the quantile residuals are addressed. The estimated survival functions for the patients are reported by age range and sex. The simulation study showed that mean squared errors decay toward zero and the average estimates converge to the true parameters when sample size increases. According to the fitted model, there is a significant difference only in the age group on the lifetime of individuals with COVID-19. Women have higher probability of recovering than men and individuals aged ≥60 years have lower recovered probabilities than those who aged <60 years. The findings suggest that the proposed model could be a good alternative to analyze censored lifetime of individuals with COVID-19.
RESUMEN
Among the models applied to analyze survival data, a standout is the inverse Gaussian distribution, which belongs to the class of models to analyze positive asymmetric data. However, the variance of this distribution depends on two parameters, which prevents establishing a functional relation with a linear predictor when the assumption of constant variance does not hold. In this context, the aim of this paper is to re-parameterize the inverse Gaussian distribution to enable establishing an association between a linear predictor and the variance. We propose deviance residuals to verify the model assumptions. Some simulations indicate that the distribution of these residuals approaches the standard normal distribution and the mean squared errors of the estimators are small for large samples. Further, we fit the new model to hospitalization times of COVID-19 patients in Piracicaba (Brazil) which indicates that men spend more time hospitalized than women, and this pattern is more pronounced for individuals older than 60 years. The re-parameterized inverse Gaussian model proved to be a good alternative to analyze censored data with non-constant variance.
RESUMEN
We propose a new continuous distribution in the interval ( 0 , 1 ) based on the generalized odd log-logistic-G family, whose density function can be symmetrical, asymmetric, unimodal and bimodal. The new model is implemented using the gamlss packages in R. We propose an extended regression based on this distribution which includes as sub-models some important regressions. We employ a frequentist and Bayesian analysis to estimate the parameters and adopt the non-parametric and parametric bootstrap methods to obtain better efficiency of the estimators. Some simulations are conducted to verify the empirical distribution of the maximum likelihood estimators. We compare the empirical distribution of the quantile residuals with the standard normal distribution. The extended regression can give more realistic fits than other regressions in the analysis of proportional data.
RESUMEN
The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.
RESUMEN
The effect of storage on mycoflora profile was monitored bimonthly in 36 corn (Zea mays L.) samples, dividing the same sample into groups dried to 11 and 14% moisture content (1008 analysis). These groups were further subdivided based on the initial total count (moulds and yeasts) up to 10(4) CFU g(-1) (12 samples, range 1.6 x 10(4) to 9.0 x 10(4), mean 3.8 x 10(4) CFU g(-1)) and up to 10(5) CFU g(-1) (24 samples, range 1.0 x 10(5) to 5.0 x 10(5), mean 2.7 x 10(5) CFU g(-1)). In the corn group dried to 11%, the fumonisin content was analysed at the initial stage (freshly harvested) and at the end of 12-month storage. Fusarium spp. and Penicillium spp. prevailed at the freshly harvested stage (100%), maintaining this profile throughout 12 months, in corn dried to both 11 and 14%. Cladosporium spp., Aspergillus spp. and Phoma spp. were also detected at lower frequencies during the storage. Fusarium spp. and the total fungal colony count during 12-month storage carried out with samples dried to 11 or 14% moisture content were statistically evaluated using ANOVA for randomized complete block design. The correlation between storage time and Fusarium spp. and total fungal colony count data was analysed by Pearson's correlation test. There was no difference in Fusarium spp. and total counts in the 10(4) CFU g(-1) initial total count group throughout the storage time (p < 0.05). There was a negative correlation between fungal population and storage time (p < 0.05) in the 10(5) CFU g(-1) initial total count group. Fumonisins were detected in all freshly harvested corn, at a mean concentration of 9.9 +/- 6.0 micro g g(-1) (range 0.74-22.6 micro g g(-)1). These values did not change in the 12-month stored corn (mean of 9.9 +/- 5.8 micro g g(-1), range 0.81-23.7 micro g g(-1)). These post harvest data indicated the importance of moisture content at the crop harvesting/predrying stage to control fungal growth and further fumonisin production.