The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

The generalized inverse Weibull distribution

The inverse Weibull distribution has the ability to model failure rates which are quite common in reliability and biological studies. A three-parameter generalized inverse Weibull distribution with decreasing and unimodal failure rate is introduced and studied. We provide a comprehensive treatment of the mathematical properties of the new distribution including expressions for the moment generating function and the rth generalized moment. The mixture model of two generalized inverse Weibull distributions is investigated. The identifiability property of the mixture model is demonstrated. For the first time, we propose a location-scale regression model based on the log-generalized inverse Weibull distribution for modeling lifetime data. In addition, we develop some diagnostic tools for sensitivity analysis. Two applications of real data are given to illustrate the potentiality of the proposed regression model.

A general hazard model for lifetime data in the presence of cure rate

Historically, the cure rate model has been used for modeling time-to-event data within which a significant proportion of patients are assumed to be cured of illnesses, including breast cancer, non-Hodgkin lymphoma, leukemia, prostate cancer, melanoma, and head and neck cancer. Perhaps the most popular type of cure rate model is the mixture model introduced by Berkson and Gage [1]. In this model, it is assumed that a certain proportion of the patients are cured, in the sense that they do not present the event of interest during a long period of time and can found to be immune to the cause of failure under study. In this paper, we propose a general hazard model which accommodates comprehensive families of cure rate models as particular cases, including the model proposed by Berkson and Gage. The maximum-likelihood-estimation procedure is discussed. A simulation study analyzes the coverage probabilities of the asymptotic confidence intervals for the parameters. A real data set on children exposed to HIV by vertical transmission illustrates the methodology.

Robust tori in a double-waved Hamiltonian model

A Hamiltonian system perturbed by two waves with particular wave numbers can present robust tori, which are barriers created by the vanishing of the perturbed Hamiltonian at some defined positions. When robust tori exist, any trajectory in phase space passing close to them is blocked by emergent invariant curves that prevent the chaotic transport. Our results indicate that the considered particular solution for the two waves Hamiltonian model shows plenty of robust tori blocking radial transport. (C) 2010 Elsevier B.V. All rights reserved.

Liquid-liquid equilibria for systems composed of refined soybean oil, free fatty acids, ethanol, and water at different temperatures

Soybean oil can be deacidified by liquid-liquid extraction with ethanol. In the present paper, the liquid-liquid equilibria of systems composed of refined soybean oil, commercial linoleic acid, ethanol and water were investigated at 298.2 K. The experimental data set obtained from the present study (at 298.2 K) and the results of Mohsen-Nia et al. [1] (at 303.2 K) and Rodrigues et al. [2] (at 323.2 K) were correlated by applying the non-random two liquid (NRTL) model. The results of the present study indicated that the mutual solubility of the compounds decreased with an increase in the water content of the solvent and a decrease in the temperature of the solution. Among variables, the water content of the solvent had the strongest effect on the solubility of the components. The maximum deviation and average variance between the experimental and calculated compositions were 1.60% and 0.89%, indicating that the model could accurately predict the behavior of the compounds at different temperatures and degrees of hydration. (C) 2010 Elsevier B.V. All rights reserved.

Laurdan Spectrum Decomposition as a Tool for the Analysis of Surface Bilayer Structure and Polarity: a Study with DMPG, Peptides and Cholesterol

The highly hydrophobic fluorophore Laurdan (6-dodecanoyl-2-(dimethylaminonaphthalene)) has been widely used as a fluorescent probe to monitor lipid membranes. Actually, it monitors the structure and polarity of the bilayer surface, where its fluorescent moiety is supposed to reside. The present paper discusses the high sensitivity of Laurdan fluorescence through the decomposition of its emission spectrum into two Gaussian bands, which correspond to emissions from two different excited states, one more solvent relaxed than the other. It will be shown that the analysis of the area fraction of each band is more sensitive to bilayer structural changes than the largely used parameter called Generalized Polarization, possibly because the latter does not completely separate the fluorescence emission from the two different excited states of Laurdan. Moreover, it will be shown that this decomposition should be done with the spectrum as a function of energy, and not wavelength. Due to the presence of the two emission bands in Laurdan spectrum, fluorescence anisotropy should be measured around 480 nm, to be able to monitor the fluorescence emission from one excited state only, the solvent relaxed state. Laurdan will be used to monitor the complex structure of the anionic phospholipid DMPG (dimyristoyl phosphatidylglycerol) at different ionic strengths, and the alterations caused on gel and fluid membranes due to the interaction of cationic peptides and cholesterol. Analyzing both the emission spectrum decomposition and anisotropy it was possible to distinguish between effects on the packing and on the hydration of the lipid membrane surface. It could be clearly detected that a more potent analog of the melanotropic hormone alpha-MSH (Ac-Ser(1)-Tyr(2)-Ser(3)-Met(4)-Glu(5)-His(6)-Phe(7)-Arg(8)-Trp(9)-Gly(10)-Lys(11)-Pro(12)-Val(13)-NH(2)) was more effective in rigidifying the bilayer surface of fluid membranes than the hormone, though the hormone significantly decreases the bilayer surface hydration.

Non-Fermi-liquid behavior in UCu(4+x)Al(8-x) compounds

We report on experimental studies of the Kondo physics and the development of non-Fermi-liquid scaling in UCu(4+x)Al(8-x) family. We studied 7 different compounds with compositions between x = 0 and 2. We measured electrical transport (down to 65 mK) and thermoelectric power (down to 1.8 K) as a function of temperature, hydrostatic pressure, and/or magnetic field. Compounds with Cu content below x = 1.25 exhibit long-range antiferromagnetic order at low temperatures. Magnetic order is suppressed with increasing Cu content and our data indicate a possible quantum critical point at x(cr) approximate to 1.15. For compounds with higher Cu content, non-Fermi-liquid behavior is observed. Non-Fermi-liquid scaling is inferred from electrical resistivity results for the x = 1.25 and 1.5 compounds. For compounds with even higher Cu content, a sharp kink occurs in the resistivity data at low temperatures, and this may be indicative of another quantum critical point that occurs at higher Cu compositions. For the magnetically ordered compounds, hydrostatic pressure is found to increase the Neel temperature, which can be understood in terms of the Kondo physics. For the non-magnetic compounds, application of a magnetic field promotes a tendency toward Fermi-liquid behavior. Thermoelectric power was analyzed using a two-band Lorentzian model, and the results indicate one fairly narrow band (10 meV and below) and a second broad band (around hundred meV). The results imply that there are two relevant energy scales that need to be considered for the physics in this family of compounds. (C) 2011 Elsevier B.V. All rights reserved.

Inferential Implications of Over-Parametrization: A Case Study in Incomplete Categorical Data

P>In the context of either Bayesian or classical sensitivity analyses of over-parametrized models for incomplete categorical data, it is well known that prior-dependence on posterior inferences of nonidentifiable parameters or that too parsimonious over-parametrized models may lead to erroneous conclusions. Nevertheless, some authors either pay no attention to which parameters are nonidentifiable or do not appropriately account for possible prior-dependence. We review the literature on this topic and consider simple examples to emphasize that in both inferential frameworks, the subjective components can influence results in nontrivial ways, irrespectively of the sample size. Specifically, we show that prior distributions commonly regarded as slightly informative or noninformative may actually be too informative for nonidentifiable parameters, and that the choice of over-parametrized models may drastically impact the results, suggesting that a careful examination of their effects should be considered before drawing conclusions.Resume Que ce soit dans un cadre Bayesien ou classique, il est bien connu que la surparametrisation, dans les modeles pour donnees categorielles incompletes, peut conduire a des conclusions erronees. Cependant, certains auteurs persistent a negliger les problemes lies a la presence de parametres non identifies. Nous passons en revue la litterature dans ce domaine, et considerons quelques exemples surparametres simples dans lesquels les elements subjectifs influencent de facon non negligeable les resultats, independamment de la taille des echantillons. Plus precisement, nous montrons comment des a priori consideres comme peu ou non-informatifs peuvent se reveler extremement informatifs en ce qui concerne les parametres non identifies, et que le recours a des modeles surparametres peut avoir sur les conclusions finales un impact considerable. Ceci suggere un examen tres attentif de l`impact potentiel des a priori.

Mixture of changing uniaxial micellar forms in lyotropic biaxial nematics

Lyotropic nematics consisting of surfactant-cosurfactant water solutions may present a biaxial phase or direct U(+) <-> U(-) transitions, in different regions of the temperature-relative concentration phase diagram, for different systems and compositions. We propose that these may be related to changes of uniaxial micellar form, which may occur either smoothly or abruptly. Smooth change of cylinder-like into disc-like shapes requires a distribution of Maier-Saupe interaction constants and we consider two limiting cases for the distribution of forms: a single Gaussian and a double Gaussian. Alternatively, an abrupt change of form is described by a discontinuous distribution of interaction constants. Our results show that the dispersive distributions yield a biaxial phase, while an abrupt change of shape leads to coexistence of uniaxial phases. Fitting the theory to the experiment for the ternary system KL/decanol/D2O leads to transition lines in very good agreement with experimental results. In order to rationalise the results of the comparison, we analyse temperature and concentration form dependence, which connects micellar and experimental macroscopic parameters. Physically consistent variations of micellar asymmetry, amphiphile partitioning and volume are obtained. To the best of the authors` knowledge, this is the first truly statistical microscopic approach that is able to model experimentally observed lyotropic biaxial nematic phases.

Phase diagram of a model for a binary mixture of nematic molecules on a Bethe lattice

We investigate the phase diagram of a discrete version of the Maier-Saupe model with the inclusion of additional degrees of freedom to mimic a distribution of rodlike and disklike molecules. Solutions of this problem on a Bethe lattice come from the analysis of the fixed points of a set of nonlinear recursion relations. Besides the fixed points associated with isotropic and uniaxial nematic structures, there is also a fixed point associated with a biaxial nematic structure. Due to the existence of large overlaps of the stability regions, we resorted to a scheme to calculate the free energy of these structures deep in the interior of a large Cayley tree. Both thermodynamic and dynamic-stability analyses rule out the presence of a biaxial phase, in qualitative agreement with previous mean-field results.

Dynamic transitions in a two dimensional associating lattice gas model

Using Monte Carlo simulations we investigate some new aspects of the phase diagram and the behavior of the diffusion coefficient in an associating lattice gas (ALG) model on different regions of the phase diagram. The ALG model combines a two dimensional lattice gas where particles interact through a soft core potential and orientational degrees of freedom. The competition between soft core potential and directional attractive forces results in a high density liquid phase, a low density liquid phase, and a gas phase. Besides anomalies in the behavior of the density with the temperature at constant pressure and of the diffusion coefficient with density at constant temperature are also found. The two liquid phases are separated by a coexistence line that ends in a bicritical point. The low density liquid phase is separated from the gas phase by a coexistence line that ends in tricritical point. The bicritical and tricritical points are linked by a critical lambda-line. The high density liquid phase and the fluid phases are separated by a second critical tau-line. We then investigate how the diffusion coefficient behaves on different regions of the chemical potential-temperature phase diagram. We find that diffusivity undergoes two types of dynamic transitions: a fragile-to-strong transition when the critical lambda-line is crossed by decreasing the temperature at a constant chemical potential; and a strong-to-strong transition when the critical tau-line is crossed by decreasing the temperature at a constant chemical potential.

Quantum system under the actions of two counteracting baths: A model for the attenuation-amplification interplay

We analyze the dynamical behavior of a quantum system under the actions of two counteracting baths: the inevitable energy draining reservoir and, in opposition, exciting the system, an engineered Glauber's amplifier. We follow the system dynamics towards equilibrium to map its distinctive behavior arising from the interplay of attenuation and amplification. Such a mapping, with the corresponding parameter regimes, is achieved by calculating the evolution of both the excitation and the Glauber-Sudarshan P function. Techniques to compute the decoherence and the fidelity of quantum states under the action of both counteracting baths, based on the Wigner function rather than the density matrix, are also presented. They enable us to analyze the similarity of the evolved state vector of the system with respect to the original one, for all regimes of parameters. Applications of this attenuation-amplification interplay are discussed.