A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.

Genetic Algorithm for the Determination of Linear Viscoelastic Relaxation Spectrum from Experimental Data

Conventional procedures employed in the modeling of viscoelastic properties of polymer rely on the determination of the polymer`s discrete relaxation spectrum from experimentally obtained data. In the past decades, several analytical regression techniques have been proposed to determine an explicit equation which describes the measured spectra. With a diverse approach, the procedure herein introduced constitutes a simulation-based computational optimization technique based on non-deterministic search method arisen from the field of evolutionary computation. Instead of comparing numerical results, this purpose of this paper is to highlight some Subtle differences between both strategies and focus on what properties of the exploited technique emerge as new possibilities for the field, In oder to illustrate this, essayed cases show how the employed technique can outperform conventional approaches in terms of fitting quality. Moreover, in some instances, it produces equivalent results With much fewer fitting parameters, which is convenient for computational simulation applications. I-lie problem formulation and the rationale of the highlighted method are herein discussed and constitute the main intended contribution. (C) 2009 Wiley Periodicals, Inc. J Appl Polym Sci 113: 122-135, 2009

EPR in the Characterization of the Shade Effect on Translucence, Remaining Free Radicals, and Polymerization Depth of Commercially Available Resin Composites

The aim of this study was to assess the relation between the number of free radicals generated and the polymerization depth in two different commercial brands of resin composites with different colors and translucence. Electron paramagnetic resonance quantified the radical populations through relative intensity (I (r)) of free radicals generated, and radical decay was monitored. Sample translucence and the classical polymerization depth were measured. The analysis indicated that resin with more color pigments (MA4, I (r) = 0.73 a.u) or more opacity components (ODA2, I (r) = 0.84 a.u) generated smaller populations of free radicals and have the lower polymerization depth than clearer (M, I (r) = 1.20 a.u and MA2, I (r) = 1.02) or more translucent (OEA2, I (r) = 1.00 a.u) composites for the same light-curing time. It seems that irradiation doses have to be adequate to more colored and less translucent resins.

EPR of gamma-induced paramagnetic centers in tellurite glasses

Intrinsic paramagnetic responses were observed in the 60TeO(2)-25ZnO-15Na(2)O and 85TeO(2)-15Na(2)O mol% glasses, after gamma-irradiation at room temperature: (1) a shoulder at g(1) = g(parallel to) = 2.02 +/- 0.01 and an estimated g(perpendicular to)similar to 2.0 attributed to tellurium-oxygen hole center (TeOHC); (2) a narrow resonance at g(2)= 1.9960 +/- 0.0005 related to the modifiers and (3) a resolved resonance at g(3) = 1.9700 +/- 0.0005 ascribed to a tellurium electron center (TeEC) of an electron trapped at an oxygen vacancy (V(o)(+)) in a tellurium oxide structural center. It is suggested that the creation of (NBO(-),V(o)(+)) pair follows a mechanism where the modifier oxide molecule actuates as a catalyser. An additional model for the NBO radiolysis produced by the gamma-irradiation is proposed on the basis of the evolution of the g(1), g(2) and g(3) intensities with increasing dose (kGy). Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.

EPR, FT-IR and XRD investigation of soils from Parana, Brazil

Samples of Araucaria area soil from Parana state, Brazil, were separated by particle size fractionation and investigated by electron paramagnetic resonance (EPR) in X-Band of 9.5 GHz at room temperature and 77K, infra-red spectroscopy and X-ray diffractometry. The paramagnetic species in the soil samples were identified by comparison with EPR spectra of some minerals studied recently by our group, several soil types and/or soil components investigated in the literature. The value of g = 2.1 (Delta H = 85 mT) indicated the presence of ferrihydrite. Hematite was identified by g = 2.1 (Delta H = 100 mT) and g = 4.3 for Fe(3+) lines of the concentrated dominium and diluted dominium. Kaolinite was identified by IR and EPR with the resonance at g = 4.3 attributed to Fe(3+) ions in isolated sites of tetrahedral and octahedral symmetry with rhombic distortion. The resonances at g = 3.7 and g = 4.9 were attributed to Fe(3+) in more highly symmetrical environment than rhombic symmetry, but not in axial symmetry. Three signals around g = 2 were attributed to radiation defects, plus additional resonances at g = 2.8 and 9.0. Signals less intense than those at g = 2.1, 3.7, and 6.5, observed for clear grains of soil, were attributed to presence of Fe(3+) in quartz which was identified by IR and XDR. (C) 2011 Elsevier B.V. All rights reserved.

Chemical process to separate iron oxides particles in pottery sample for EPR dating

Ancient potteries usually are made of the local clay material, which contains relatively high concentration of iron. The powdered samples are usually quite black, due to magnetite, and, although they can be used for thermoluminescene (TL) dating, it is easiest to obtain better TL reading when clearest natural or pre-treated sample is used. For electron paramagnetic resonance (EPR) measurements, the huge signal due to iron spin-spin interaction, promotes an intense interference overlapping any other signal in this range. Sample dating is obtained by dividing the radiation dose, determined by the concentration of paramagnetic species generated by irradiation, by the natural dose so as a consequence, EPR dating cannot be used, since iron signal do not depend on radiation dose. In some cases, the density separation method using hydrated solution of sodium polytungstate [Na(G)(H(2)W(12)O(40))center dot H(2)O] becomes useful. However, the sodium polytungstate is very expensive in Brazil: hence an alternative method for eliminating this interference is proposed. A chemical process to eliminate about 90% of magnetite was developed. A sample of powdered ancient pottery was treated in a mixture (3:1:1) of HCI, HNO(3) and H(2)O(2) for 4 h. After that, it was washed several times in distilled water to remove all acid matrixes. The original black sample becomes somewhat clearer. The resulting material was analyzed by plasma mass spectrometry (ICP-MS), with the result that the iron content is reduced by a factor of about 9. In EPR measurements a non-treated natural ceramic sample shows a broad spin-spin interaction signal, the chemically treated sample presents a narrow signal in g= 2.00 region, possibly due to a radical of (SiO(3))(3-), mixed with signal of remaining iron [M. lkeya, New Applications of Electron Spin Resonance, World Scientific, Singapore, 1993, p. 285]. This signal increases in intensity under -gamma-irradiation. However, still due to iron influence, the additive method yielded too old age-value. Since annealing at 300 degrees C, Toyoda and Ikeya IS. Toyoda, M. Ikeya, Geochem. J. 25 (1991) 427-445] states that E `(1)-signal with maximum intensity is obtained, while annealing at 400 degrees C E`(1)-signal is completely eliminated, the subtraction of the second one from 300 degrees C heat-treated sample isolate E`(1)-like signal. Since this is radiation dose-dependent, we show that now EPR dating becomes possible. (C) 2008 Elsevier B.V. All rights reserved.

TL, EPR and optical absorption in natural grossular crystal

Grossular is one of six members of silicate Garnet group. Two samples GI and GII have been investigated concerning their luminescence thermally stimulated (TL). EPR and optical absorption and the measurements were carried out to find out whether or not same point defects are responsible for all three properties. Although X-rays diffraction analysis has shown that both GI and GII have practically the same crystal structure of a standard grossular crystal, they presented different behavior in many aspects. The TL glow curve shape, TL response to radiation dose, the effect of annealing at high temperatures before irradiation, the dependence of UV bleaching parameters on peak temperature, all of them differ going from GI to GII. The EPR signals around g = 2.0 as well as at g = 4.3 and 6.0 have much larger intensity in GI than in GII. Very high temperature (> 800 degrees C annealing causes large increase in the bulk background absorption in GI, however, only very little in GII. In the cases of EPR and optical absorption, the difference in their behavior can be attributed to Fe3+ ions; however, in the TL case one cannot and the cause was not found as yet. (C) 2008 Elsevier B.V. All rights reserved.

Deviance residuals in generalised log-gamma regression models with censored observations

In this article, we compare three residuals based on the deviance component in generalised log-gamma regression models with censored observations. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and the empirical distribution of each residual is displayed and compared with the standard normal distribution. For all cases studied, the empirical distributions of the proposed residuals are in general symmetric around zero, but only a martingale-type residual presented negligible kurtosis for the majority of the cases studied. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended for the martingale-type residual in generalised log-gamma regression models with censored data. A lifetime data set is analysed under log-gamma regression models and a model checking based on the martingale-type residual is performed.

Improved likelihood inference for the shape parameter in Weibull regression

We obtain adjustments to the profile likelihood function in Weibull regression models with and without censoring. Specifically, we consider two different modified profile likelihoods: (i) the one proposed by Cox and Reid [Cox, D.R. and Reid, N., 1987, Parameter orthogonality and approximate conditional inference. Journal of the Royal Statistical Society B, 49, 1-39.], and (ii) an approximation to the one proposed by Barndorff-Nielsen [Barndorff-Nielsen, O.E., 1983, On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343-365.], the approximation having been obtained using the results by Fraser and Reid [Fraser, D.A.S. and Reid, N., 1995, Ancillaries and third-order significance. Utilitas Mathematica, 47, 33-53.] and by Fraser et al. [Fraser, D.A.S., Reid, N. and Wu, J., 1999, A simple formula for tail probabilities for frequentist and Bayesian inference. Biometrika, 86, 655-661.]. We focus on point estimation and likelihood ratio tests on the shape parameter in the class of Weibull regression models. We derive some distributional properties of the different maximum likelihood estimators and likelihood ratio tests. The numerical evidence presented in the paper favors the approximation to Barndorff-Nielsen`s adjustment.

Measuring time series predictability using support vector regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.

On beta regression residuals

We propose two new residuals for the class of beta regression models, and numerically evaluate their behaviour relative to the residuals proposed by Ferrari and Cribari-Neto. Monte Carlo simulation results and empirical applications using real and simulated data are provided. The results favour one of the residuals we propose.