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

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.

The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil.

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.

A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution.

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.

The semiparametric regression model for bimodal data with different penalized smoothers applied to climatology, ethanol and air quality data.

Semiparametric regressions can be used to model data when covariables and the response variable have a nonlinear relationship. In this work, we propose three flexible regression models for bimodal data called the additive, additive partial and semiparametric regressions, basing on the odd log-logistic generalized inverse Gaussian distribution under three types of penalized smoothers, where the main idea is not to confront the three forms of smoothings but to show the versatility of the distribution with three types of penalized smoothers. We present several Monte Carlo simulations carried out for different configurations of the parameters and some sample sizes to verify the precision of the penalized maximum-likelihood estimators. The usefulness of the proposed regressions is proved empirically through three applications to climatology, ethanol and air quality data.

Joint regression modeling of location and scale parameters of the skew t distribution with application in soil chemistry data.

In regression model applications, the errors may frequently present a symmetric shape. In such cases, the normal and Student t distributions are commonly used. In this paper, we shall be concerned only to model heavy-tailed, skewed errors and absence of variance homogeneity with two regression structures based on the skew t distribution. We consider a classic analysis for the parameters of the proposed model. We perform a diagnostic analysis based on global influence and quantile residuals. For different parameter settings and sample sizes, various simulation results are obtained and compared to evaluate the performance of the skew t regression. Further, we illustrate the usefulness of the new regression by means of a real data set (amount of potassium in different soil areas) from a study carried out at the Department of Soil Science of the Luiz de Queiroz School of Agriculture, University of São Paulo.

A new regression model for rates and proportions data with applications.

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.

A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil.

A heteroscedastic regression based on the odd log-logistic Marshall-Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.

The effect of host plants on Tetranychus evansi, Tetranychus urticae (Acari: Tetranychidae) and on their fungal pathogen Neozygites floridana (Entomophthorales: Neozygitaceae).

In a series of tritrophic-level interaction experiments, the effect of selected host plants of the spider mites, Tetranychus evansi and Tetranychus urticae, on Neozygites floridana was studied by evaluating the attachment of capilliconidia, presence of hyphal bodies in the infected mites, mortality from fungal infection, mummification and sporulation from fungus-killed mite cadavers. Host plants tested for T. evansi were tomato, cherry tomato, eggplant, nightshade, and pepper while host plants tested for T. urticae were strawberry, jack bean, cotton and Gerbera. Oviposition rate of the mites on each plant was determined to infer host plant suitability while host-switching determined antibiosis effect on fungal activity. T. evansi had a high oviposition on eggplant, tomato and nightshade but not on cherry tomato and pepper. T. urticae on jack bean resulted in a higher oviposition than on strawberry, cotton and Gerbera. Attachment of capilliconidia to the T. evansi body, presence of hyphal bodies in infected T. evansi and mortality from fungal infection were significantly higher on pepper, nightshade and tomato. The highest level of T. evansi mummification was observed on tomato. T. evansi cadavers from tomato and eggplant produced more primary conidia than those from cherry tomato, nightshade and pepper. Switching N. floridana infected T. evansi from one of five Solanaceous host plants to tomato had no prominent effect on N. floridana performance. For T. urticae, strawberry and jack bean provided the best N. floridana performance when considering all measured parameters. Strawberry also had the highest primary conidia production. This study shows that performance of N. floridana can vary with host plants and may be an important factor for the development of N. floridana epizootics.

Effect of temperature on sporulation of Neozygites floridana isolates from different climates and their virulence against the tomato red spider mite, Tetranychus evansi.

The fungal pathogen Neozygites floridana Weiser and Muma has been evaluated as a classical biological candidate for introduction into Africa against the invasive tomato red spider mite Tetranychus evansi Baker and Pritchard. In this study, the effect of temperature on sporulation, germination and virulence of three isolates of N. floridana collected from T. evansi in three climatically distinct regions of Brazil and Argentina was determined. Six constant temperatures of 13 degrees C, 17 degrees C, 21 degrees C, 25 degrees C, 29 degrees C and 33 degrees C were tested for their effect on the ability of the three fungal isolates to sporulate, germinate and kill the mites. Six alternating-temperature regimes of 17-13 degrees C, 21-13 degrees C, 29-13 degrees C, 33-13 degrees C, 33-23 degrees C, 33-28 degrees C under a 12h photophase were also tested to estimate virulence of the three isolates against T. evansi. The Vipos isolate discharged more conidia than isolates from Recife or Piracicaba at all temperatures and sporulation was strongly temperature dependent. Optimal sporulation rates were observed at 25 degrees C while optimal germination rates were observed at 25 degrees C and 29 degrees C. At 29 degrees C, the shortest mean survival time of T. evansi (3.16 days, 95% CI of 3.05-3.27) was observed for the isolate from Vipos, while the longest LT(50) (3.47 days, 95% CI 3.34-3.59) was observed for the isolate from Piracicaba. Mortality of mites increased as the differences between alternating day and night temperatures increased from 8 degrees C (21-13 degrees C), to 10 degrees C (33-23 degrees C), to 16 degrees C (29-13 degrees C), with smallest and highest temperature differences of 4 degrees C (17-13 degrees C) and 20 degrees C (33-13 degrees C), both producing low mortalities. The overall results suggest that the Vipos isolate is better adapted to a wider range of temperatures than the other isolates tested.

Analysis of shielding calculation methods for 16- and 64-slice computed tomography facilities.

The new multislice computed tomography (CT) machines require some new methods of shielding calculation, which need to be analysed. NCRP Report No. 147 proposes three shielding calculation methods based on the following dosimetric parameters: weighted CT dose index for the peripheral axis (CTDI(w, per)), dose-length product (DLP) and isodose maps. A survey of these three methods has been carried out. For this analysis, we have used measured values of the dosimetric quantities involved and also those provided by the manufacturer, making a comparison between the results obtained. The barrier thicknesses when setting up two different multislice CT instruments, a Philips Brilliance 16 or a Philips Brilliance 64, in the same room, are also compared. Shielding calculation from isodose maps provides more reliable results than the other two methods, since it is the only method that takes the actual scattered radiation distribution into account. It is concluded therefore that the most suitable method for calculating the barrier thicknesses of the CT facility is the one based on isodose maps. This study also shows that for different multislice CT machines the barrier thicknesses do not necessarily become bigger as the number of slices increases, because of the great dependence on technique used in CT protocols for different anatomical regions.

The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis.

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.

Location-scale mixed models and goodness-of-fit assessment applied to insect ecology.

Survival models have been extensively used to analyse time-until-event data. There is a range of extended models that incorporate different aspects, such as overdispersion/frailty, mixtures, and flexible response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of the results found in the case study.

Mycoflora and occurrence of aflatoxin B(1) and fumonisin B(1) during storage of Brazilian sorghum.

The present study is a 1-year follow up of the mycoflora of 140 samples of Brazilian freshly harvested (10) and stored (130) sorghum, the levels of aflatoxin and fumonisin contamination detected in the grains, and the prevailing abiotic factors (grain moisture content, water activity, temperature, relative humidity, and mean rainfall) at the time of sampling. The results show a predominance of the genera Phoma (57.1%), Aspergillus (42.7%), Fusarium (25.0%), and Rhizopus (21.4%) and the presence of nine other filamentous fungi. Fusarium, Aspergillus, and Penicillium, the three most important genera in terms of toxicity, presented numbers of colony forming units per gram of sorghum (CFU/g) that varied from 1 x 10(3) to 36 x 10(3), from 1 x 10(3) to 295 x 10(3), and from 1 x 10(3) to 20 x 10(3) CFU/g, respectively. The species most frequently found were Aspergillus flavus and Fusarium moniliforme. Of the total samples analyzed, 12.8% were contaminated with aflatoxin B(1) (concentration mean = 7-33 microg/kg) and 74.2% with fumonisin B(1) (concentration mean = 0.11-0.15 microg/g). This paper is the first report of the natural occurrence of aflatoxins and fumonisins in sorghum grain from Brazil.