Exploratory Approach to the use of Techniques of Regression Analysis and Pooled Analysis in Studies of Consumer Behavior

This paper is part of a large study to assess the adequacy of the use of multivariate statistical techniques in theses and dissertations of some higher education institutions in the area of marketing with theme of consumer behavior from 1997 to 2006. The regression and conjoint analysis are focused on in this paper, two techniques with great potential of use in marketing studies. The objective of this study was to analyze whether the employement of these techniques suits the needs of the research problem presented in as well as to evaluate the level of success in meeting their premisses. Overall, the results suggest the need for more involvement of researchers in the verification of all the theoretical precepts of application of the techniques classified in the category of investigation of dependence among variables.

Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression

This paper addresses the investment decisions considering the presence of financial constraints of 373 large Brazilian firms from 1997 to 2004, using panel data. A Bayesian econometric model was used considering ridge regression for multicollinearity problems among the variables in the model. Prior distributions are assumed for the parameters, classifying the model into random or fixed effects. We used a Bayesian approach to estimate the parameters, considering normal and Student t distributions for the error and assumed that the initial values for the lagged dependent variable are not fixed, but generated by a random process. The recursive predictive density criterion was used for model comparisons. Twenty models were tested and the results indicated that multicollinearity does influence the value of the estimated parameters. Controlling for capital intensity, financial constraints are found to be more important for capital-intensive firms, probably due to their lower profitability indexes, higher fixed costs and higher degree of property diversification.

Regression of glomerular injury by losartan in experimental diabetic nephropathy

Many features of chronic kidney disease may be reversed, but it is unclear whether advanced lesions, such as adhesions of sclerotic glomerular tufts to Bowman`s capsule (synechiae), can resolve during treatment. We previously showed, using a renal ablation model, that the renoprotective effect of the AT-1 receptor blocker, losartan, is dose-dependent. Here we determined if moderate and advanced glomerular lesions, associated with streptozotocin-induced diabetes, regress with conventional or high-dose losartan treatment. Using daily insulin injection for 10 months, we maintained diabetic adult male Munich-Wistar rats in a state of moderate hyperglycemia. Following this period, some rats continued to receive insulin with or without conventional or high-dose losartan for an additional 2 months. Diabetic rats pretreated with insulin for 10 months and age-matched non-diabetic rats served as controls. Mesangial expansion was found in the control diabetic rats and was exacerbated in those rats maintained on only insulin for an additional 2 months. Conventional and high-dose losartan treatments reduced this mesangial expansion and the severity of synechiae lesions below that found prior to treatment; however, the frequency of the latter was unchanged. There was no dose-response effect of losartan. Our results show that regression of mesangial expansion and contraction of sclerotic lesions is feasible in the treatment of diabetes, but complete resolution of advanced glomerulosclerosis may be hard to achieve.

Expression of fibroblast growth factor receptors during development and regression of the bovine corpus luteum

There is evidence that fibroblast growth factors (FGFs) are involved in the regulation of growth and regression of the corpus luteum (CL). However, the expression pattern of most FGF receptors (FGFRs) during CL lifespan is still unknown. The objective of the present study was to determine the pattern of expression of `B` and `C` splice variants of FGFRs in the bovine CL. Bovine CL were collected from an abattoir and classed as corpora hemorrhagica (Stage I), developing (Stage II), developed (Stage III) or regressed (Stage IV) CL. Expression of FGFR mRNA was measured by semiquantitative reverse transcription-polymerase chain reaction and FGFR protein was localised by immunohistochemistry. Expression of mRNA encoding the `B` and `C` spliced forms of FGFR1 and FGFR2 was readily detectable in the bovine CL and was accompanied by protein localisation. FGFR1C and FGFR2C mRNA expression did not vary throughout CL lifespan, whereas FGFR1B was upregulated in the developed (Stage III) CL. FGFR3B, FGFR3C and FGFR4 expression was inconsistent in the bovine CL. The present data indicate that FGFR1 and FGFR2 splice variants are the main receptors for FGF action in the bovine CL.

Orthodontic-surgical treatment of Class III malocclusion with extraction of an impacted canine and multi-segmented maxillary surgery

Establishment of a treatment plan is based on efficacy and easy application by the clinician, and acceptance by the patient. Treatment of adult patients with Class III malocclusion might require orthognathic surgery, especially when the deformity is severe, with a significant impact on facial esthetics. Impacted teeth can remarkably influence treatment planning, which should be precise and concise to allow a reasonably short treatment time with low biologic cost. We report here the case of a 20-year-old man who had a skeletal Class III malocclusion and impaction of the maxillary right canine, leading to remarkable deviation of the maxillary midline; this was his chief complaint. Because of the severely deviated position of the impacted canine, treatment included extraction of the maxillary right canine and left first premolar for midline correction followed by leveling, alignment, correction of compensatory tooth positioning, and orthognathic surgery to correct the skeletal Class III malocclusion because of the severe maxillary deficiency. This treatment approach allowed correction of the maxillary dental midline discrepancy to the midsagittal plane and establishment of good occlusion and optimal esthetics. (Am J Orthod Dentofacial Orthop 2010;137:840-9)

Proximal arterial vasoconstriction precedes regression of the hyaloid vasculature

Purpose: To determine whether constriction of proximal arterial vessels precedes involution of the distal hyaloid vasculature in the mouse, under normal conditions, and whether this vasoconstriction is less pronounced when the distal hyaloid network persists, as it does in oxygen-induced retinopathy (OIR). Methods: Photomicrographs of the vasa hyaloidea propria were analysed from pre-term pups (1-2 days prior to birth), and on Days 1-11 post-birth. The OIR model involved exposing pups to similar to 90% O-2 from D1-5, followed by return to ambient air. At sampling times pups were anaesthetised and perfused with india ink. Retinal flatmounts were also incubated with FITC-lectin (BS-1, G. simplicifolia,); this labels all vessels, allowing identification of vessels not patent to the perfusate. Results: Mean diameter of proximal hyaloid vessels in preterm pups was 25.44 +/- 1.98 mum; +/-1 SEM). Within 3-12 hrs of birth, significant vasoconstriction was evident (diameter:12.45 +/- 0.88 mum), and normal hyaloid regression subsequently occurred. Similar vasoconstriction occurred in the O-2-treated group, but this was reversed upon return to room air, with significant dilation of proximal vessels by D7 (diameter: 31.75 +/- 11.99 mum) and distal hyaloid vessels subsequently became enlarged and tortuous. Conclusions: Under normal conditions, vasoconstriction of proximal hyaloid vessels occurs at birth, preceding attenuation of distal hyaloid vessels. Vasoconstriction also occurs in O-2-treated pups during treatment, but upon return to room air, the remaining hyaloid vessels dilate proximally, and the distal vessels become dilated and tortuous. These observations support the contention that regression of the hyaloid network is dependent, in the first instance, on proximal arterial vasoconstriction.

Endemism and cryptogenesis in 'segmented' mites: A review of Australian Alicorhagiidae, Terpnacaridae, Oehserchestidae and Grandjeanicidae (Acari : sarcoptiformes)

Endeostigmata are early derivative acariform mites, fossils of which are known from the Devonian. Extant species bear numerous plesiomorphies, the most striking being remnant opisthosomal segmentation. Also, many are all-female parthenogens with broad geographical distributions. Many of the species reported in the present study may represent clones of ancient Gondwana species. Before the present study only a handful of endeostigmatans had been reported from Australia. A key to the families of Endeostigmata is provided in the present paper, along with a review of the Australian fauna of the families Alicorhagiidae (new record), Grandjeanicidae (new record), Oehserchestidae (new record), and Terpnacaridae. Terpnacarus gibbosus (Womersley) is redescribed. A report of the first records of the cosmopolitan parthenogens Alicorhagia usitata Theron et al., Alycosmesis palmata (Oudemans), Stigmalychus veretrum Theron et al., Terpnacarus carolinaensis Theron, and Oehserchestes arboriger (Theron) in Australia is provided, along with a description of the new species Grandjeanicus theroni (Grandjeanicidae). Terpnacarus variolus Shiba and T. glebulentus Theron are junior synonyms of T. gibbosus.

Model specification tests in nonparametric stochastic regression models

In this paper, we consider testing for additivity in a class of nonparametric stochastic regression models. Two test statistics are constructed and their asymptotic distributions are established. We also conduct a small sample study for one of the test statistics through a simulated example. (C) 2002 Elsevier Science (USA).

An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data

We consider a mixture model approach to the regression analysis of competing-risks data. Attention is focused on inference concerning the effects of factors on both the probability of occurrence and the hazard rate conditional on each of the failure types. These two quantities are specified in the mixture model using the logistic model and the proportional hazards model, respectively. We propose a semi-parametric mixture method to estimate the logistic and regression coefficients jointly, whereby the component-baseline hazard functions are completely unspecified. Estimation is based on maximum likelihood on the basis of the full likelihood, implemented via an expectation-conditional maximization (ECM) algorithm. Simulation studies are performed to compare the performance of the proposed semi-parametric method with a fully parametric mixture approach. The results show that when the component-baseline hazard is monotonic increasing, the semi-parametric and fully parametric mixture approaches are comparable for mildly and moderately censored samples. When the component-baseline hazard is not monotonic increasing, the semi-parametric method consistently provides less biased estimates than a fully parametric approach and is comparable in efficiency in the estimation of the parameters for all levels of censoring. The methods are illustrated using a real data set of prostate cancer patients treated with different dosages of the drug diethylstilbestrol. Copyright (C) 2003 John Wiley Sons, Ltd.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

3D-2D image registration by nonlinear regression

We propose a 3D-2D image registration method that relates image features of 2D projection images to the transformation parameters of the 3D image by nonlinear regression. The method is compared with a conventional registration method based on iterative optimization. For evaluation, simulated X-ray images (DRRs) were generated from coronary artery tree models derived from 3D CTA scans. Registration of nine vessel trees was performed, and the alignment quality was measured by the mean target registration error (mTRE). The regression approach was shown to be slightly less accurate, but much more robust than the method based on an iterative optimization approach.

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

Disparities in cervical and breast cancer mortality in Brazil

OBJECTIVE To analyze cervical and breast cancer mortality in Brazil according to socioeconomic and welfare indicators. METHODS Data on breast and cervical cancer mortality covering a 30-year period (1980-2010) were analyzed. The data were obtained from the National Mortality Database, population data from the Brazilian Institute of Geography and Statistics database, and socioeconomic and welfare information from the Institute of Applied Economic Research. Moving averages were calculated, disaggregated by capital city and municipality. The annual percent change in mortality rates was estimated by segmented linear regression using the joinpoint method. Pearson’s correlation coefficients were conducted between average mortality rate at the end of the three-year period and selected indicators in the state capital and each Brazilian state. RESULTS There was a decline in cervical cancer mortality rates throughout the period studied, except in municipalities outside of the capitals in the North and Northeast. There was a decrease in breast cancer mortality in the capitals from the end of the 1990s onwards. Favorable socioeconomic indicators were inversely correlated with cervical cancer mortality. A strong direct correlation was found with favorable indicators and an inverse correlation with fertility rate and breast cancer mortality in inner cities. CONCLUSIONS There is an ongoing dynamic process of increased risk of cervical and breast cancer and attenuation of mortality because of increased, albeit unequal, access to and provision of screening, diagnosis and treatment. 

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.