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.

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.

Longitudinal study of diarrhoeal disease in a peri-urban community in Manaus (Amazon-Brazil).

A 20-month longitudinal study of diarrhoeal disease was carried out in a poor peri-urban community of Manaus (Amazon-Brazil), and the attack rate of this disease ranged from 0.2 to 4.8 episodes of diarrhoea per person per year. The age group most affected was 0 to 35 months old. A probable aetiological agent was identified in 68 of the 110 faecal samples collected. The most frequent enteropathogens isolated were enterotoxigenic Escherichia coli and Giardia lamblia. Enterotoxigenic Escherichia coli was also isolated from stream-water and from flies collected inside a household.

Bacterias enteropatogenicas de diarreia infantil aguda em Tucuruy, Para. / Enteropathogenic bacteria of acute infantile diarrhea in Tucurui, Para, Brazil

Foram efetuados exames bacteriologicos e parasitologicos de fezes de 110 criancas diarreicas na faixa etaria de 0 - 10 anos, atendidas em 2 (dois) hospitais de Tucurui, Para.As amostras foram examinadas antes de qualquer tratamento medico. Em 51 casos (46,3%) foram identificados bacterias e/ou parasitas intestinais, sendo 17 casos (15,4%) bacterias 28 casos (25,4%) parasitas e 6 casos (5,4%) bacterias e parasitas. Patogenos bacterianos foram isolados em 23 amostras (20,9%): 21 Escherichia coli sorogrupos patogenicos, 1 Shigella sonnei e 1 Shigella flexneri. Oito sorogrupos diferentes de E. coli foram identificados. O sorogrupo de maior frequencia foi o 026K60 seguido pelo 086a:K61. Todas as cepas patogenicas com excecao de E. coli 0119:K69 apresentaram resistencia multipla frente a 12 drogas antimicrobianas. Foram os seguintes os parasitas intestinais identificados: Giardia lamblia 15,4%, Ascaris lumbricoides 10,0%, Entamoeba histolytica 6,3%, Strongyloides stercoralis 4,5%, Trichocephalus trichiurus 2,7%, Ancilostoma sp 1,8%, Taenia sp 0,9%, Balantidium coli 0,9% e Himenolepis nana 0,9%