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.

Power series generalized nonlinear models

We introduce in this paper a new class of discrete generalized nonlinear models to extend the binomial, Poisson and negative binomial models to cope with count data. This class of models includes some important models such as log-nonlinear models, logit, probit and negative binomial nonlinear models, generalized Poisson and generalized negative binomial regression models, among other models, which enables the fitting of a wide range of models to count data. We derive an iterative process for fitting these models by maximum likelihood and discuss inference on the parameters. The usefulness of the new class of models is illustrated with an application to a real data set. (C) 2008 Elsevier B.V. All rights reserved.

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.

Immunological parameters related to the adjuvant effect of the ordered mesoporous silica SBA-15

In 2006, the first report of a nanostructured material as adjuvant was described establishing the effectiveness of the ordered mesoporous SBA-15 silica as an immune adjuvant. The present study evaluated the SBA-15 capacity to modulate the immune responsiveness of High and Low responder mice immunized with BSA encapsulated/adsorbed in SBA-15 by the intramuscular or oral route and the adjuvant effect was compared with the responsiveness induced by BSA in aluminum hydroxide salts or emulsified in Incomplete Freund adjuvant. These results demonstrate the ability of the non-toxic SBA-15 nanoparticles to increase the immunogenicity and repair the responsiveness of the constitutively low responder individuals inducing both the IgG2a and the IgG1 isotypes, independently of the immune cell committed and conditioning the low phenotype. This new adjuvant may reveal novel therapeutic targets for immune modulation and vaccine design. (C) 2010 Elsevier Ltd. All rights reserved.

Synthesis, characterization and catalytic evaluation of cubic ordered mesoporous iron-silicon oxides

Iron was successfully incorporated in FDU-1 type cubic ordered mesoporous silica by a simple direct synthesis route. The (Fe/FDU-1) samples were characterized by Rutherford back-scattering spectrometry (RBS), small angle X-ray scattering (SAXS). N(2) sorption isotherm, X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). The resulting material presented an iron content of about 5%. Prepared at the usual acid pH of -0.3, the composite was mostly formed by amorphous silica and hematite with a quantity of Fe(2+) present in the structure. The samples prepared with adjusted pH values (2 and 3.5) were amorphous. The samples` average pore diameter was around 12.0 nm and BET specific surface area was of 680 m(2) g(-1). Although the iron-incorporated material presented larger lattice parameter, about 25 nm compared to pure FDU-1, the Fe/FDU-1 composite still maintained its cubic ordered fcc mesoporous structure before and after the template removal at 540 degrees C. The catalytic performance of Fe/FDU-1 was investigated in the catalytic oxidation of Black Remazol B dye using a catalytic ozonation process. The results indicated that Fe/FDU-1 prepared at the usual acid pH exhibited high catalytic activity in the mineralization of this pollutant when compared to the pure FDU-1. Fe(2)O(3) and Fe/FDU-1 prepared with higher pH of 2 and 3.5. (C) 2010 Elsevier B.V. All rights reserved.

Immobilization of glucose oxidase enzyme (GOD) in large pore ordered mesoporous cage-like FDU-1 silica

Large pore ordered mesoporous silica FDU-1 with three-dimensional (3D) face-centered cubic, Fm3m arrangement of rnesopores, was synthesized under strong acid media using B-50-6600 poly(ethylene oxide)-poly(butylene oxide)-poly(ethylene oxide) triblock copolymer (EO(39)BO(47)EO(39)), tetraethyl orthosilicate (TEOS) and trimethyl-benzene (TMB). Large pore FDU-1 silica was obtained by using the following gel composition 1TEOS:0.00735B50-6600:0.00735TMB:6HCl:155H(2)O. The pristine material exhibited a BET specific surface area of 684 m(2) g(-1), total pore volume of 0.89 cm(3) g(-1), external surface area of 49 m(2) g(-1) and microporous volume of 0.09 cm(3) g(-1). The enzyme activity was determined by the Flow Injection Analysis-Chemiluminescence (FIA-CL) method. For GOD immobilized on the FDU-1 silica, GOD supernatant and GOD solution, the FIA-CL results were 9.0, 18.6 and 34.0 U, respectively. The value obtained for the activity of the GOD solution with FIA-CL method is in agreement with the 35 U, obtained by spectrophotometry. (C) 2011 Elsevier B.V. All rights reserved.

Electrical transport in disordered and ordered magnetic domains under pressures and magnetic fields

Magnetic M( T, H, P) and electrical transport.( T, H, P) measurements in a strong spin-lattice-charge coupled La(0.7)Ca(0.3)MnO(3) system have been conducted. The application of H and P leads to the formation of different magnetic domain structures in the vicinity and below the polaronic-to-ferromagnetic transition temperature. The charge mobility is more sensitive to the variation of the spatial wave function overlap between Mn(3+) eg and O(2-) 2p orbitals due to the applied compacting pressure rather than the relative spin orientation between neighbouring Mn ions when the magnetic field is applied. In spite of the presence of different magnetic domain structures due to the sample history, the effect of magnetic field and pressure is less pronounced at lower temperatures on electrical transport properties.

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.

Improved score tests in symmetric linear regression models

The class of symmetric linear regression models has the normal linear regression model as a special case and includes several models that assume that the errors follow a symmetric distribution with longer-than-normal tails. An important member of this class is the t linear regression model, which is commonly used as an alternative to the usual normal regression model when the data contain extreme or outlying observations. In this article, we develop second-order asymptotic theory for score tests in this class of models. We obtain Bartlett-corrected score statistics for testing hypotheses on the regression and the dispersion parameters. The corrected statistics have chi-squared distributions with errors of order O(n(-3/2)), n being the sample size. The corrections represent an improvement over the corresponding original Rao`s score statistics, which are chi-squared distributed up to errors of order O(n(-1)). Simulation results show that the corrected score tests perform much better than their uncorrected counterparts in samples of small or moderate size.

Size and power properties of some tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders distribution has been used quite effectively to model times to failure for materials subject to fatigue and for modeling lifetime data. In this paper we obtain asymptotic expansions, up to order n(-1/2) and under a sequence of Pitman alternatives, for the non-null distribution functions of the likelihood ratio, Wald, score and gradient test statistics in the Birnbaum-Saunders regression model. The asymptotic distributions of all four statistics are obtained for testing a subset of regression parameters and for testing the shape parameter. Monte Carlo simulation is presented in order to compare the finite-sample performance of these tests. We also present two empirical applications. (C) 2010 Elsevier B.V. All rights reserved.

Signed likelihood ratio tests in the Birnbaum-Saunders regression model

The Birnbaum-Saunders regression model is becoming increasingly popular in lifetime analyses and reliability studies. In this model, the signed likelihood ratio statistic provides the basis for testing inference and construction of confidence limits for a single parameter of interest. We focus on the small sample case, where the standard normal distribution gives a poor approximation to the true distribution of the statistic. We derive three adjusted signed likelihood ratio statistics that lead to very accurate inference even for very small samples. Two empirical applications are presented. (C) 2010 Elsevier B.V. All rights reserved.

Improved likelihood inference in beta regression

We consider the issue of performing accurate small-sample likelihood-based inference in beta regression models, which are useful for modelling continuous proportions that are affected by independent variables. We derive small-sample adjustments to the likelihood ratio statistic in this class of models. The adjusted statistics can be easily implemented from standard statistical software. We present Monte Carlo simulations showing that inference based on the adjusted statistics we propose is much more reliable than that based on the usual likelihood ratio statistic. A real data example is presented.