Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Three Bartlett-type corrections for score statistics in symmetric nonlinear regression models

We present simple matrix formulae for corrected score statistics in symmetric nonlinear regression models. The corrected score statistics follow more closely a chi (2) distribution than the classical score statistic. Our simulation results indicate that the corrected score tests display smaller size distortions than the original score test. We also compare the sizes and the powers of the corrected score tests with bootstrap-based score tests.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Bayesian Analysis for the Generalized Lognormal Distribution Applied to Failure Time Analysis

There are several versions of the lognormal distribution in the statistical literature, one is based in the exponential transformation of generalized normal distribution (GN). This paper presents the Bayesian analysis for the generalized lognormal distribution (logGN) considering independent non-informative Jeffreys distributions for the parameters as well as the procedure for implementing the Gibbs sampler to obtain the posterior distributions of parameters. The results are used to analyze failure time models with right-censored and uncensored data. The proposed method is illustrated using actual failure time data of computers.

Versatile microanalytical system with porous polypropylene capillary membrane for calibration gas generation and trace gaseous pollutants sampling applied to the analysis of formaldehyde, formic acid, acetic acid and ammonia in outdoor air

The analytical determination of atmospheric pollutants still presents challenges due to the low-level concentrations (frequently in the mu g m(-3) range) and their variations with sampling site and time In this work a capillary membrane diffusion scrubber (CMDS) was scaled down to match with capillary electrophoresis (CE) a quick separation technique that requires nothing more than some nanoliters of sample and when combined with capacitively coupled contactless conductometric detection (C(4)D) is particularly favorable for ionic species that do not absorb in the UV-vis region like the target analytes formaldehyde formic acid acetic acid and ammonium The CMDS was coaxially assembled inside a PTFE tube and fed with acceptor phase (deionized water for species with a high Henry s constant such as formaldehyde and carboxylic acids or acidic solution for ammonia sampling with equilibrium displacement to the non-volatile ammonium ion) at a low flow rate (8 3 nLs(-1)) while the sample was aspirated through the annular gap of the concentric tubes at 25 mLs(-1) A second unit in all similar to the CMDS was operated as a capillary membrane diffusion emitter (CMDE) generating a gas flow with know concentrations of ammonia for the evaluation of the CMDS The fluids of the system were driven with inexpensive aquarium air pumps and the collected samples were stored in vials cooled by a Peltier element Complete protocols were developed for the analysis in air of NH(3) CH(3)COOH HCOOH and with a derivatization setup CH(2)O by associating the CMDS collection with the determination by CE-C(4)D The ammonia concentrations obtained by electrophoresis were checked against the reference spectrophotometric method based on Berthelot s reaction Sensitivity enhancements of this reference method were achieved by using a modified Berthelot reaction solenoid micro-pumps for liquid propulsion and a long optical path cell based on a liquid core waveguide (LCW) All techniques and methods of this work are in line with the green analytical chemistry trends (C) 2010 Elsevier B V All rights reserved

Simultaneous determination of chromium and manganese in alumina by slurry sampling graphite furnace atomic absorption spectrometry using NbC and NaF as modifiers

This paper reports a method for the direct and simultaneous determination of Cr and Mn in alumina by slurry sampling graphite furnace atomic absorption spectrometry (SiS-SIMAAS) using niobium carbide (NbC) as a graphite platform modifier and sodium fluoride (NaF) as a matrix modifier. 350 mu g of Nb were thermally deposited on the platform surface allowing the formation of NbC (mp 3500 degrees C) to minimize the reaction between aluminium and carbon of the pyrolytic platform, improving the graphite tube lifetime up to 150 heating cycles. A solution of 0.2 mol L(-1) NaF was used as matrix modifier for alumina dissolution as cryolite-based melt, allowing volatilization during pyrolysis step. Masses (c.a. 50 mg) of sample were suspended in 30 ml of 2.0% (v/v) of HNO(3). Slurry was manually homogenized before sampling. Aliquots of 20 mu l of analytical solutions and slurry samples were co-injected into the graphite tube with 20 mu l of the matrix modifier. In the best conditions of the heating program, pyrolysis and atomization temperatures were 1300 degrees C and 2400 degrees C, respectively. A step of 1000 degrees C was optimized allowing the alumina dissolution to form cryolite. The accuracy of the proposed method has been evaluated by the analysis of standard reference materials. The found concentrations presented no statistical differences compared to the certified values at 95% of the confidence level. Limits of detection were 66 ng g(-1) for Cr and 102 ng g(-1) for Mn and the characteristic masses were 10 and 13 pg for Cr and Mn, respectively.

Feasibility of using in situ fusion for the determination of Co, Cr and Mn in Portland cement by direct solid sampling graphite furnace atomic absorption spectrometry

In situ fusion on the boat-type graphite platform has been used as a sample pretreatment for the direct determination of Co, Cr and Mn in Portland cement by solid sampling graphite furnace atomic absorption spectrometry (SS-GF AAS). The 3-field Zeeman technique was adopted for background correction to decrease the sensitivity during measurements. This strategy allowed working with up to 200 mu g of sample. The in situ fusion was accomplished using 10 mu L of a flux mixture 4.0% m/v Na(2)CO(3) + 4.0% m/v ZnO + 0.1% m/v Triton (R) X-100 added over the cement sample and heated at 800 degrees C for 20 s. The resulting mould was completely dissolved with 10 mu L of 0.1% m/v HNO(3). Limits of detection were 0.11 mu g g(-1) for Co, 1.1 mu g g(-1) for Cr and 1.9 mu g g(-1) for Mn. The accuracy of the proposed method has been evaluated by the analysis of certified reference materials. The values found presented no statistically significant differences compared to the certified values (Student`s t-test, p<0.05). In general, the relative standard deviation was lower than 12% (n = 5). (C) 2009 Elsevier B.V. All rights reserved.

Environmental formaldehyde analysis by active diffusive sampling with a bundle of polypropylene porous capillaries followed by capillary zone electrophoretic separation and contactless conductivity detection

Compared to other volatile carbonylic compounds present in outdoor air, formaldehyde (CH2O) is the most toxic, deserving more attention in terms of indoor and outdoor air quality legislation and control. The analytical determination of CH2O in air still presents challenges due to the low-level concentration (in the sub-ppb range) and its variation with sampling site and time. Of the many available analytical methods for carbonylic compounds, the most widespread one is the time consuming collection in cartridges impregnated with 2,4-dinitrophenylhydrazine followed by the analysis of the formed hydrazones by HPLC. The present work proposes the use of polypropylene hollow porous capillary fibers to achieve efficient CH2O collection. The Oxyphan (R) fiber (designed for blood oxygenation) was chosen for this purpose because it presents good mechanical resistance, high density of very fine pores and high ratio of collection area to volume of the acceptor fluid in the tube, all favorable for the development of air sampling apparatus. The collector device consists of a Teflon pipe inside of which a bundle of polypropylene microporous capillary membranes was introduced. While the acceptor passes at a low flow rate through the capillaries, the sampled air circulates around the fibers, impelled by a low flow membrane pump (of the type used for aquariums ventilation). The coupling of this sampling technique with the selective and quantitative determination of CH2O, in the form of hydroxymethanesulfonate (HMS) after derivatization with HSO3-, by capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-(CD)-D-4) enabled the development of a complete analytical protocol for the CH2O evaluation in air. (C) 2008 Published by Elsevier B.V.

Direct determination of iron in sand using solid sampling graphite furnace atomic absorption spectrometry

A fast and reliable method for the direct determination of iron in sand by solid sampling graphite furnace atomic absorption spectrometry was developed. A Zeeman-effect 3-field background corrector was used to decrease the sensitivity of spectrometer measurements. This strategy allowed working with up to 200 mu g of samples, thus improving the representativity. Using samples with small particle sizes (1-50 mu m) and adding 5 mu g Pd as chemical modifier, it was possible to obtain suitable calibration curves with aqueous reference solutions. The pyrolysis and atomization temperatures for the optimized heating program were 1400 and 2500 degrees C, respectively. The characteristic mass, based on integrated absorbance, was 56 pg, and the detection limits, calculated considering the variability of 20 consecutive measurements of platform inserted without sample was 32 pg. The accuracy of the procedure was checked with the analysis of two reference materials (IPT 62 and 63). The determined concentrations were in agreement with the recommended values (95% confidence level). Five sand samples were analyzed, and a good agreement (95% confidence level) was observed using the proposed method and conventional flame atomic absorption spectrometry. The relative standard deviations were lower than 25% (n = 5). The tube and boat platform lifetimes were around 1000 and 250 heating cycles, respectively.

Application of solid sampling device for direct determination of chromium and nickel in lubricating oils by graphite furnace atomic absorption spectrometry

One method using a solid sampling device for the direct determination of Cr and Ni in fresh and used lubricating oils by graphite furnace atomic absorption spectrometry are proposed. The high organic content in the samples was minimized using a digestion step at 400 degrees C in combination with an oxidant mixture 1.0% (v v(-1)) HNO3+15% (v v(-1)) H2O2+0.1% (m v(-1)) Triton X-100 for the in situ digestion. The 3-field mode Zeeman-effect allowed the spectrometer calibration up to 5 ng of Cr and Ni. The quantification limits were 0.86 mu g g(-1) for Cr and 0.82 mg g(-1) for Ni, respectively. The analysis of reference materials showed no statistically significant difference between the recommended values and those obtained by the proposed methods.