A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap 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. Further, for different parameter settings, sample sizes, and censoring percentages, several 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 extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

Sudden onset of log-periodicity and superdiffusion in non-Markovian random walks with amnestically induced persistence: exact results

Random walks can undergo transitions from normal diffusion to anomalous diffusion as some relevant parameter varies, for instance the L,vy index in L,vy flights. Here we derive the Fokker-Planck equation for a two-parameter family of non-Markovian random walks with amnestically induced persistence. We investigate two distinct transitions: one order parameter quantifies log-periodicity and discrete scale invariance in the first moment of the propagator, whereas the second order parameter, known as the Hurst exponent, describes the growth of the second moment. We report numerical and analytical results for six critical exponents, which together completely characterize the properties of the transitions. We find that the critical exponents related to the diffusion-superdiffusion transition are identical in the positive feedback and negative feedback branches of the critical line, even though the former leads to classical superdiffusion whereas the latter gives rise to log-periodic superdiffusion.

Validation of ACB in vitro and in vivo as a biomagnetic method for measuring stomach contraction

Background The aim of this study was to validate a biomagnetic method (alternate current biosusceptometry, ACB) for monitoring gastric wall contractions in rats. Methods In vitro data were obtained to establish the relationship between ACB and the strain-gauge (SG) signal amplitude. In vivo experiments were performed in pentobarbital-anesthetized rats with SG and magnetic markers previously implanted under the gastric serosa or after ingestion of magnetic material. Gastric motility was quantified from the tracing amplitudes and frequency profiles obtained by Fast Fourier Transform. Key Results The correlation between in vitro signal amplitudes was strong (R = 0.989). The temporal cross-correlation coefficient between the ACB and SG signal amplitude was higher (P < 0.0001) in the postprandial (88.3 +/- 9.1 V) than in the fasting state (31.0 +/- 16.9 V). Irregular signal profiles, low contraction amplitudes, and smaller signal-to-noise ratios explained the poor correlation between techniques for fasting-state recordings. When a magnetic material was ingested, there was also strong correlation in the frequency and signal amplitude and a small phase-difference between the techniques. The contraction frequencies using ACB were 0.068 +/- 0.007 Hz (postprandial) and 0.058 +/- 0.007 Hz (fasting) (P < 0.002) and those using SG were 0.066 +/- 0.006 Hz (postprandial) and 0.059 +/- 0.008 Hz (fasting) (P < 0.005). Conclusions & Inferences In summary, ACB is reliable for monitoring gastric wall contractions using both implanted and ingested magnetic materials, and may serve as an accurate and sensitive technique for gastrointestinal motility studies.

Tracking the Ovarian Cycle in Black-and-Gold Howlers (Alouatta caraya) by Measuring Fecal Steroids and Observing Vaginal Bleeding

A better understanding of a species` reproductive physiology can help conservation programs to manage primates in the wild and develop assisted reproductive technologies in captivity. We investigated whether measurements of fecal progestin and estrogen metabolites obtained by a radioimmunoassay could be used to monitor the ovarian cycle of Alouatta caraya. We also compared the occurrence of vaginal bleeding with the hormone profiles. We collected fecal samples from 3 adult and 1 subadult captive female over 5 mo and performed vaginal cytology for the adults. The interval between fecal progestin surges in the adult females was 19.11 +/- 2.14 d (n = 18 cycles). Fecal progestin concentrations remained at basal values for 9.83 +/- 2.21 d (n = 18) and rose to elevated values for 9.47 +/- 0.72 d (n = 19). The subadult female showed basal levels of fecal estrogen and progestin concentrations throughout the study, suggesting that our hormone measurements are valid to monitor the ovarian cycle. Bleeding periods coincided with basal levels of fecal estrogens and progestin at intervals of 19.8 +/- 0.9 d and lasted for 4.1 +/- 1.0 d. Although we obtained these data from only 3 individuals, the results indicate that this species likely has a menstrual-type ovarian cycle. These data provide the first endocrine profile for the Alouatta caraya ovarian cycle and are similar to results obtained for other howler species. This similarity is important for comparative studies of howlers, allowing for a better understanding of their reproductive physiology and contributing to a critical information base for managing Alouatta species.

Measuring Bond Strength Between Fiber Post and Root Dentin: A Comparison of Different Tests

Purpose The aim of this study was to evaluate the ability of bond strength tests to accurately measure the bond strength of fiber posts luted into root canals Materials and Methods The test methods studied were hourglass microtensile (HM), push-out (PS), modified push out (MP) and pull out (PL) The evaluated parameters were bond strength values, reliability (using Weibull analysis), failure mode (using confocal microscopy), and stress distribution (using finite element analysis) Forty human intact single rooted and endodontically treated teeth were divided into four groups Each group was assigned one of the test methods The samples in the HM and PS groups were 1 0 +/- 0 1 mm thick, the HM samples were hourglass shaped and the PS samples were disk shaped For the PL and MP groups, each 1 mm dentin slice was luted with a fiber post piece Three dimensional models of each group were made and stress was analyzed based on Von Mises criteria Results PL provided the highest values of bond strength followed by MP both of which also had greater amounts of adhesive failures PS showed the highest frequency of cohesive failures MP showed a more homogeneous stress distribution and a higher Weibull modulus Conclusion The specimen design directly influences the biomechanical behavior of bond strength tests

Nasal mucus transportability in children with cleft palate

Objectives: Children with cleft palate (CP) have a high prevalence of sinusitis. Considering that nasal mucus properties play a pivotal role in the upper airway defense mechanism, the aim of the study was to evaluate nasal mucus transportability and physical properties from children with CP. Setting: Hospital for Rehabilitation of Craniofacial Anomalies, School of Dentistry, University of Sao Paulo, Bauru, SP, Brazil and Laboratory of Experimental Air Pollution, School of Medicine, University of Sao Paulo, Sao Paulo, SP, Brazil. Methods: Nasal mucus samples were collected by nasal aspiration from children with CP and without CP (non-CP). Sneeze clearance (SC) was evaluated by the simulated sneeze machine. In vitro mucus transportability (MCT) by cilia was evaluated by the frog palate preparation. Mucus physical surface properties were assessed by measuring the contact angle (CA). Mucus rheology was determined by means of a magnetic rheometer, and the results were expressed as log G* (vectorial sum of viscosity and elasticity) and tan delta (relationship between viscosity and elasticity) measured at 1 and 100 rad/s. Results: Mucus samples from children with CP had a higher SC than non-CP children (67 +/- 30 and 41 +/- 24 mm, respectively, p < 0.05). Mucus samples from children with CP had a lower CA (24 +/- 16 degrees and 35 +/- 11 degrees, p < 0.05) and a higher tan delta 100 (0.79 +/- 0.24 and 0.51 +/- 0.12, p < 0.05) than non-CP children. There were no significant differences in mucus MCT, log G* 1, tan delta 1 and log G* 100 obtained for CP and non-CP children. Conclusions: Nasal mucus physical properties from children with CP are associated with higher sneeze transportability. The high prevalence of sinusitis in children with CP cannot be explained by changes in mucus physical properties and transportability. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

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.

Metal location and thickness in a multilayered sheet by measuring K alpha/K beta, L alpha/L beta and L alpha/L gamma X-ray ratios

When a multilayered material is analyzed by means of energy-dispersive X-ray fluorescence analysis, then the X-ray ratios of K alpha/K beta, or L alpha/L beta and L alpha/L gamma, for an element in the multilayered material, depend on the composition and thickness of the layer in which the element is situated, and on the composition and thickness of the superimposed layer (or layers). Multilayered samples are common in archaeometry, for example, in the case of pigment layers in paintings, or in the case of gilded or silvered alloys. The latter situation is examined in detail in the present paper, with a specific reference to pre-Columbian alloys from various museums in the north of Peru. (C) 2009 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.

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.

The log-bimodal-skew-normal model. A geochemical application

The main objective of this paper is to study a logarithm extension of the bimodal skew normal model introduced by Elal-Olivero et al. [1]. The model can then be seen as an alternative to the log-normal model typically used for fitting positive data. We study some basic properties such as the distribution function and moments, and discuss maximum likelihood for parameter estimation. We report results of an application to a real data set related to nickel concentration in soil samples. Model fitting comparison with several alternative models indicates that the model proposed presents the best fit and so it can be quite useful in real applications for chemical data on substance concentration. Copyright (C) 2011 John Wiley & Sons, Ltd.