Estimation and diagnostics for heteroscedastic nonlinear regression models based on scale mixtures of skew-normal distributions

An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression models under scale mixtures of skew-normal (SMSN) distributions is developed. This novel class of models provides a useful generalization of the heteroscedastic symmetrical nonlinear regression models (Cysneiros et al., 2010), since the random term distributions cover both symmetric as well as asymmetric and heavy-tailed distributions such as skew-t, skew-slash, skew-contaminated normal, among others. A simple EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters is presented and the observed information matrix is derived analytically. In order to examine the performance of the proposed methods, some simulation studies are presented to show the robust aspect of this flexible class against outlying and influential observations and that the maximum likelihood estimates based on the EM-type algorithm do provide good asymptotic properties. Furthermore, local influence measures and the one-step approximations of the estimates in the case-deletion model are obtained. Finally, an illustration of the methodology is given considering a data set previously analyzed under the homoscedastic skew-t nonlinear regression model. (C) 2012 Elsevier B.V. All rights reserved.

Bartlett corrections in Birnbaum-Saunders nonlinear regression models

Lemonte and Cordeiro [Birnbaum-Saunders nonlinear regression models, Comput. Stat. Data Anal. 53 (2009), pp. 4441-4452] introduced a class of Birnbaum-Saunders (BS) nonlinear regression models potentially useful in lifetime data analysis. We give a general matrix Bartlett correction formula to improve the likelihood ratio (LR) tests in these models. The formula is simple enough to be used analytically to obtain several closed-form expressions in special cases. Our results generalize those in Lemonte et al. [Improved likelihood inference in Birnbaum-Saunders regressions, Comput. Stat. DataAnal. 54 (2010), pp. 1307-1316], which hold only for the BS linear regression models. We consider Monte Carlo simulations to show that the corrected tests work better than the usual LR tests.

Removal of Pb2+ from aqueous solutions using two Brazilian rocks containing zeolites

The removal of Pb2+ from aqueous solution by two Brazilian rocks that contain zeolites-amygdaloidal dacite (ZD) and sandstone (ZS)-was examined by batch experiments. ZD contains mordenite and ZS, stilbite. The effects of contact time, concentration of metal in solution and capacity of Na+ to recover the adsorbed metals were evaluated at room temperature (20A degrees C). The sorption equilibrium was reached in the 30 min of agitation time. Both materials removed 100% of Pb2+ from solutions at concentrations up to 50 mg/L, and at concentrations larger than 100 mg/L of Pb2+, the adsorption capacity of sandstone was more efficient than that of amygdaloidal dacite due to the larger quantities and the type of zeolites (stilbite) in the cement of this rock. All adsorbed Pb2+ was easily replaced by Na+ in both samples. The analysis of the adsorption models using nonlinear regression revealed that the Sips and the Freundlich isotherms provided the best fit for the ZS and ZD experimental data, respectively, indicating the heterogeneous adsorption surfaces of these zeolites.

In vitro action of antiparasitic drugs, especially artesunate, against Toxoplasma gondii

Introduction: Toxoplasmosis is usually a benign infection, except in the event of ocular, central nervous system (CNS), or congenital disease and particularly when the patient is immunocompromised. Treatment consists of drugs that frequently cause adverse effects; thus, newer, more effective drugs are needed. In this study, the possible activity of artesunate, a drug successfully being used for the treatment of malaria, on Toxoplasma gondii growth in cell culture is evaluated and compared with the action of drugs that are already being used against this parasite. Methods: LLC-MK2 cells were cultivated in RPMI medium, kept in disposable plastic bottles, and incubated at 36 degrees C with 5% CO2. Tachyzoites of the RH strain were used. The following drugs were tested: artesunate, cotrimoxazole, pentamidine, pyrimethamine, quinine, and trimethoprim. The effects of these drugs on tachyzoites and LLC-MK2 cells were analyzed using nonlinear regression analysis with Prism 3.0 software. Results: Artesunate showed a mean tachyzoite inhibitory concentration (IC50) of 0.075 mu M and an LLC MK2 toxicity of 2.003 mu M. Pyrimethamine was effective at an IC50 of 0.482 mu M and a toxicity of 11.178 mu M. Trimethoprim alone was effective against the in vitro parasite. Cotrimoxazole also was effective against the parasite but at higher concentrations than those observed for artesunate and pyrimethamine. Pentamidine and quinine had no inhibitory effect over tachyzoites. Conclusions: Artesunate is proven in vitro to be a useful alternative for the treatment of toxoplasmosis, implying a subsequent in vivo effect and suggesting the mechanism of this drug against the parasite.

Influence diagnostics in Gaussian spatial linear models

Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.

Numerical analysis of control surface effects on AUV

Computational fluid dynamics, CFD, is becoming an essential tool in the prediction of the hydrodynamic efforts and flow characteristics of underwater vehicles for manoeuvring studies. However, when applied to the manoeuvrability of autonomous underwater vehicles, AUVs, most studies have focused on the de- termination of static coefficients without considering the effects of the vehicle control surface deflection. This paper analyses the hydrodynamic efforts generated on an AUV considering the combined effects of the control surface deflection and the angle of attack using CFD software based on the Reynolds-averaged Navier–Stokes formulations. The CFD simulations are also independently conducted for the AUV bare hull and control surface to better identify their individual and interference efforts and to validate the simulations by comparing the experimental results obtained in a towing tank. Several simulations of the bare hull case were conducted to select the k –ω SST turbulent model with the viscosity approach that best predicts its hydrodynamic efforts. Mesh sensitivity analyses were conducted for all simulations. For the flow around the control surfaces, the CFD results were analysed according to two different methodologies, standard and nonlinear. The nonlinear regression methodology provides better results than the standard methodology does for predicting the stall at the control surface. The flow simulations have shown that the occurrence of the control surface stall depends on a linear relationship between the angle of attack and the control surface deflection. This type of information can be used in designing the vehicle’s autopilot system.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.