A Nonsmooth Two-Sex Population Model
Eduardo Garibaldi, Marcelo Sobottka
This paper considers a two-dimensional logistic model to study populations with two genders.The growth behavior of a population is guided by two coupled ordinary differential equations given by a non-differentiable vector field whose parameters are the secondary sex ratio (the ratio of males to females at time of birth), inter- and intra-gender competitions, fertility rates and a mating function. Using geometrical techniques, we analyze the singularities and the basin of attraction of the system, determining the relationships between the parameters for which the system presents an equilibrium point. In particular, we describe conditions on the secondary sex ratio and discuss the role of the median number of female sexual partners of each male for the conservation of a two-sex species.
Application of prediction models using fuzzy sets: an Bayesian inspired approach
Felipo Bacani, Laécio C. Barros
A fuzzy inference framework based on fuzzy relations is explored and applied to a real set of simulated forecasts and experimental data referring to temperature and humidity in specific coffee crop sites in Brazil. In short, the used model consists of fuzzy relations over possibility distributions, resulting in a fuzzy model analog to a Bayesian inference process. The application of the fuzzy model to temperature and humidity data resulted in a set of revised forecasts, which were later compared to the correspondent set of experimental data using two different statistical measures of accuracy, MAPE (mean absolute percentage error) and Willmott D. Statistical results were confronted to the original simulated forecast fit to experimental data, showing that the methodology was, in most cases, able to improve the specialist’s forecasts in both statistical measures.
Augmented mixed beta regression models for periodontal proportion data
Diana M. Galvis, Dipankar Badyophadyay, Víctor H. Lachos
Continuous (clustered) proportion data often arise in various domains of medicine andpublic health where the response variable of interest is a proportion (or percentage) quantify-ing disease status for the cluster units, ranging between zero and one. However, due to thepresence of relatively disease-free as well as highly diseased subjects in any study, the proportion values can lie in the interval [0, 1]. While Beta regression can be adapted for assessing covariate effects here, it’s versatility is often challenged due to the presence/excess of zeros and ones because the Beta support lies in the interval (0, 1). To circumvent this, we augment the probabilities of zero and one with the Beta density, controlling for the clustering effect.Our approach is Bayesian with the ability to borrow information across various stages of thecomplex model hierarchy, and produces a computationally convenient framework amenable toavailable freeware. The marginal likelihood is tractable, and can be used to develop Bayesiancase-deletion influence diagnostics based on q-divergence measures. Both simulation studiesand application to a real dataset from a clinical periodontology study quantify the gain in modelfit and parameter estimation over other adhoc alternatives, and provide quantitative insight intoassessing the true covariate effects on the proportion responses.
La Construcción de la Definición Axiomática de Probabilidad
Mario A. Gneri
On Non-Smooth Perturbations of Degenerate or Non-Degenerate Planar Centers
Douglas D. Novaes
We provide sufficient conditions for the existence of limit cycles of non–smooth perturbed planar centers, when the set of discontinuity is an algebraic variety. It is introduced a mechanism which allows us to deal with such system, even in higher dimension. The main tool used in this paper is the averaging method. Two applications are given in careful detail.
Generalized Linear Mixed Models for Correlated Binary Data with T-link
Denise Reis Costa, Marcos O. Prates
A critical issue in modelling binary response data is the choice of the links. We introducea new link based on the Student t-distribution (t-link) for correlated binary data. The t-link relates to the common probit-normal link adding one additional parameter which purely controlsthe heaviness of the tails of the link. We propose an interesting EM algorithm for computingthe maximum likelihood for generalized linear mixed t-link models for correlated binary data.In contrast with recent developments (Tan et al., 2007; Meza et al., 2009), this algorithm usesclosed-form expressions at the E-step, as opposed to Monte Carlo simulation. Our proposedalgorithm rely on available formulas for the mean and variance of a truncated multivariatet-distribution. To illustrate the new methodology, a real data set on respiratory infection inchildren and a simulation study are presented.
Limit Cycles for m-Piecewise Discontinuous Polynomial Liénard Differential Equations
Jaume Llibre, Marco A. Teixeira
We provide lower bounds for the maximum number of limit cycles for the m–piecewise discontinuous polynomial differential equations x = y + sgn(gm (x, y))F (x), y = −x, where the zero set of the function sgn(gm (x, y)) with m = 2, 4, 6, . . . is the product of m/2 straight lines passing through the origin of coordinates dividing the plane in sectors of angle 2π/m, and sgn(z) denotes the sign function.
Periodic Solutions of Discontinuous Second Order Differential Systems
Jaume Llibre, Marco A. Teixeira
We provide sufficient conditions por the existence of periodic solutions of some classes of autonomous and non–autonomous second order differential equations with discontinuous right–hand sides. In the plane the discontinuities considered are given by the straight lines either x = 0, or xy = 0. Two applications of these results are made, one to control systems with variable structure, and the other to small external periodic excitation of a discontinuous nonlinear oscillator.
Physical Assets Replacement: An Analytical Approach
Igor G. Cesca, Douglas D. Novaes
The economic life of an asset is the optimum length of its use-fulness, which is the moment that the asset’s expenses are minimum. In this paper, the economic life of physical assets, such as industry machine and equipment, can be interpreted as the moment that the minimum is reached by its equivalent property cost function, defined as the sum of all equivalent capital and maintenance costs during its life.Many authors in classical papers have used principles of engineering economic to solve the assets replacement problem. However, in the literature, the main attributes found were proved with intuitive ideas instead mathematical analysis. Therefore, in this paper the main goal is to study these principles of engineering economic with mathematical techniques.Here, is used non-smooth analysis to cla sify all the possibilities for the minimum of a class of equivalent property cost functions of assets. The minimum of these function gives the optimum moment for the asset to be replaced, i.e., its economic life.
Bayesian Inference in Nonlinear Mixed-Effects Models Using Normal Independent Distributions
Víctor H. Lachos, Luis M. Castro, Dipak K. Dey
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