The role of clouds in improving the regression model for hourly values of diffuse solar radiation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Conway-Maxwell-Poisson-generalized gamma regression model with long-term survivors

In this paper, we proposed a flexible cure rate survival model by assuming the number of competing causes of the event of interest following the Conway-Maxwell distribution and the time for the event to follow the generalized gamma distribution. This distribution can be used to model survival data when the hazard rate function is increasing, decreasing, bathtub and unimodal-shaped including some distributions commonly used in lifetime analysis as particular cases. Some appropriate matrices are derived in order to evaluate local influence on the estimates of the parameters by considering different perturbations, and some global influence measurements are also investigated. Finally, data set from the medical area is analysed.

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Choosing between Cox proportional hazards and logistic models for interval-censored data via bootstrap

This work develops a new methodology in order to discriminate models for interval-censored data based on bootstrap residual simulation by observing the deviance difference from one model in relation to another, according to Hinde (1992). Generally, this sort of data can generate a large number of tied observations and, in this case, survival time can be regarded as discrete. Therefore, the Cox proportional hazards model for grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless, 1982) can befitted by means of generalized linear models. Whitehead (1989) considered censoring to be an indicative variable with a binomial distribution and fitted the Cox proportional hazards model using complementary log-log as a link function. In addition, a logistic model can be fitted using logit as a link function. The proposed methodology arises as an alternative to the score tests developed by Colosimo et al. (2000), where such models can be obtained for discrete binary data as particular cases from the Aranda-Ordaz distribution asymmetric family. These tests are thus developed with a basis on link functions to generate such a fit. The example that motivates this study was the dataset from an experiment carried out on a flax cultivar planted on four substrata susceptible to the pathogen Fusarium oxysoprum. The response variable, which is the time until blighting, was observed in intervals during 52 days. The results were compared with the model fit and the AIC values.

Some practical advice on polynomial regression analysis from blocked response surface designs

It is often necessary to run response surface designs in blocks. In this paper the analysis of data from such experiments, using polynomial regression models, is discussed. The definition and estimation of pure error in blocked designs are considered. It is recommended that pure error is estimated by assuming additive block and treatment effects, as this is more consistent with designs without blocking. The recovery of inter-block information using REML analysis is discussed, although it is shown that it has very little impact if thc design is nearly orthogonally blocked. Finally prediction from blocked designs is considered and it is shown that prediction of many quantities of interest is much simpler than prediction of the response itself.

Determining the shape of the universe using discrete sources

Suppose we have identified three clusters of galaxies as being topological copies of the same object. How does this information constrain the possible models for the shape of our universe? It is shown here that, if our universe has flat spatial sections, these multiple images can be accommodated within any of the six classes of compact orientable three-dimensional flat space forms. Moreover, the discovery of two more triples of multiple images in the neighbourhood of the first one would allow the determination of the topology of the universe, and in most cases the determination of its size.

The flavor problem and discrete symmetries

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Carbon Nuclear Magnetic Resonance Spectroscopic Profiles coupled to Partial Least-Squares Multivariate Regression for Prediction of Several Physicochemical Parameters of Brazilian Commercial Gasoline

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian binary regression model: an application to in-hospital death after AMI prediction

Um modelo bayesiano de regressão binária é desenvolvido para predizer óbito hospitalar em pacientes acometidos por infarto agudo do miocárdio. Métodos de Monte Carlo via Cadeias de Markov (MCMC) são usados para fazer inferência e validação. Uma estratégia para construção de modelos, baseada no uso do fator de Bayes, é proposta e aspectos de validação são extensivamente discutidos neste artigo, incluindo a distribuição a posteriori para o índice de concordância e análise de resíduos. A determinação de fatores de risco, baseados em variáveis disponíveis na chegada do paciente ao hospital, é muito importante para a tomada de decisão sobre o curso do tratamento. O modelo identificado se revela fortemente confiável e acurado, com uma taxa de classificação correta de 88% e um índice de concordância de 83%.

Correlation models of diffuse solar-radiation applied to the city of São Paulo, Brazil

Measurements of global and diffuse solar-radiation, at the Earth's surface, carried out from May 1994 to June 1999 in São Paulo City, Brazil, were used to develop correlation models to estimate hourly, daily and monthly values of diffuse solar-radiation on horizontal surfaces. The polynomials derived by linear regression fitting were able to model satisfactorily the daily and monthly values of diffuse radiation. The comparison with models derived for other places demonstrates some differences related mainly to altitude effects. (C) 2002 Elsevier B.V. Ltd. All rights reserved.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

A partial least squares and principal component regression study of quinone compounds with trypanocidal activity

A quantitative structure-activity relationship (QSAR) study of 19 quinone compounds with trypanocidal activity was performed by Partial Least Squares (PLS) and Principal Component Regression (PCR) methods with the use of leave-one-out crossvalidation procedure to build the regression models. The trypanocidal activity of the compounds is related to their first cathodic potential (Ep(c1)). The regression PLS and PCR models built in this study were also used to predict the Ep(c1) of six new quinone compounds. The PLS model was built with three principal components that described 96.50% of the total variance and present Q(2) = 0.83 and R-2 = 0.90. The results obtained with the PCR model were similar to those obtained with the PLS model. The PCR model was also built with three principal components that described 96.67% of the total variance with Q(2) = 0.83 and R-2 = 0.90. The most important descriptors for our PLS and PCR models were HOMO-1 (energy of the molecular orbital below HOMO), Q4 (atomic charge at position 4), MAXDN (maximal electrotopological negative difference), and HYF (hydrophilicity index).

Thermodynamic models for water sorption by grape skin and pulp

The enthalpy-entropy compensation theory was applied to water sorption for grapes of Italy variety. The moisture sorption isotherms were analyzed using the static gravimetric method at 35, 40, 50, 60, 70 and 75 degrees C. For isotherms construction, the skin and pulp of the grape were used separately and it was possible to observe significant differences. The GAB equation was fitted to the experimental data, using direct nonlinear regression analysis; the agreement between experimental and calculated values was satisfactory. The net isosteric heat or enthalpy of water sorption, determined from the equilibrium sorption data, showed a different behavior when compared with other works, as it was obtained for skin and pulp separately. Plots of Delta h vs Delta S for skin and pulp provided the isokinetic temperatures T-Bs = 423.2 +/- 27.6 K and T-Bp = 424.5 +/- 25.3 K, respectively, indicating an enthalpy-controlled desorption process over the whole range of moisture content considered.

The Geometric Birnbaum-Saunders regression model with cure rate

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)