Likelihood approximations and discrete models for tied survival data

Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

Particular solution for anomalous diffusion equation with source term

In literature the phenomenon of diffusion has been widely studied, however for nonextensive systems which are governed by a nonlinear stochastic dynamic, there are a few soluble models. The purpose of this study is to present the solution of the nonlinear Fokker-Planck equation for a model of potential with barrier considering a term of absorption. Systems of this nature can be observed in various chemical or biological processes and their solution enriches the studies of existing nonextensive systems.

Integrable aspects of the scaling q-state Potts models II: finite-size effects

We continue our discussion of the q-state Potts models for q less than or equal to 4, in the scaling regimes close to their critical and tricritical points. In a previous paper, the spectrum and full S-matrix of the models on an infinite line were elucidated; here, we consider finite-size behaviour. TBA equations are proposed for all cases related to phi(21) and phi(12) perturbations of unitary minimal models. These are subjected to a variety of checks in the ultraviolet and infrared limits, and compared with results from a recently-proposed non-linear integral equation. A non-linear integral equation is also used to study the flows from tricritical to critical models, over the full range of q. Our results should also be of relevance to the study of the off-critical dilute A models in regimes 1 and 2. (C) 2003 Elsevier B.V. B.V. All rights reserved.

Reconstruction of interacting dark energy models from parametrizations

Models with interacting dark energy can alleviate the cosmic coincidence problem by allowing dark matter and dark energy to evolve in a similar fashion. At a fundamental level, these models are specified by choosing a functional form for the scalar potential and for the interaction term. However, in order to compare to observational data it is usually more convenient to use parametrizations of the dark energy equation of state and the evolution of the dark matter energy density. Once the relevant parameters are fitted, it is important to obtain the shape of the fundamental functions. In this paper I show how to reconstruct the scalar potential and the scalar interaction with dark matter from general parametrizations. I give a few examples and show that it is possible for the effective equation of state for the scalar field to cross the phantom barrier when interactions are allowed. I analyze the uncertainties in the reconstructed potential arising from foreseen errors in the estimation of fit parameters and point out that a Yukawa-like linear interaction results from a simple parametrization of the coupling.

Mathematical models of generalized diffusion

We discuss in this paper equations describing processes involving non-linear and higher-order diffusion. We focus on a particular case (u(t) = 2 lambda (2)(uu(x))(x) + lambda (2)u(xxxx)), which is put into analogy with the KdV equation. A balance of nonlinearity and higher-order diffusion enables the existence of self-similar solutions, describing diffusive shocks. These shocks are continuous solutions with a discontinuous higher-order derivative at the shock front. We argue that they play a role analogous to the soliton solutions in the dispersive case. We also discuss several physical instances where such equations are relevant.

Bicomplexes and conservation laws in non-Abelian Toda models

A bicomplex structure is associated with the Leznov-Saveliev equation of integrable models. The linear problem associated with the zero-curvature condition is derived in terms of the bicomplex linear equation. The explicit example of a non-Abelian conformal affine Toda model is discussed in detail and its conservation laws are derived from the zero-curvature representation of its equation of motion.

The higher grading structure of the WKI hierarchy and the two-component short pulse equation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

EXOTIC CARBON SYSTEMS IN TWO-NEUTRON HALO THREE-BODY MODELS

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Higher order Painleve equations and their symmetries via reductions of a class of integrable models

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Inverse scattering approach for massive Thirring models with integrable type-II defects

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Light composite Higgs boson from the normalized Bethe-Salpeter equation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Generalized neighbor-interaction models induced by nonlinear lattices

It is shown that the tight-binding approximation of the nonlinear Schrodinger equation with a periodic linear potential and periodic in space nonlinearity coefficient gives rise to a number of nonlinear lattices with complex, both linear and nonlinear, neighbor interactions. The obtained lattices present nonstandard possibilities, among which we mention a quasilinear regime, where the pulse dynamics obeys essentially the linear Schrodinger equation. We analyze the properties of such models both in connection to their modulational stability, as well as in regard to the existence and stability of their localized solitary wave solutions.

Mini-implant and Nance button for initial retraction of maxillary canines: a prospective study in cast models

OBJETIVO: a ancoragem óssea é fundamental para o sucesso do tratamento de algumas más oclusões, pois permite a aplicação de forças contínuas, diminui o tempo de tratamento e independe da colaboração do paciente. MÉTODOS: o propósito desse trabalho foi comparar, por meio de modelos dentários, a perda de ancoragem após a retração inicial de caninos superiores entre dois grupos. O grupo A utilizou o mini-implante enquanto o grupo B utilizou o Botão de Nance. Para todos os pacientes foram realizados dois modelos (M1 e M2). Os primeiros modelos foram realizados ao início (M1), e os outros ao final da retração inicial de canino (M2). RESULTADOS: todas as medidas foram tabuladas e submetidas à análise estatística. Para verificar o erro sistemático intraexaminador foi utilizado o teste t pareado. Na determinação do erro casual utilizou-se o cálculo de erro proposto por Dahlberg. Para comparação entre as fases Início e Após, foi utilizado o teste t pareado. Para a comparação entre os grupos de mini-implante e Botão de Nance, foi utilizado o teste t de Student para medidas independentes. em todos os testes foi adotado nível de significância de 5% (p<0,05). CONCLUSÃO: ao se medir e comparar em modelos dentários a perda de ancoragem dos molares após a retração inicial de canino utilizando-se dois sistemas de ancoragem distintos (Mini-implante e Botão de Nance), pôde-se observar a inexistência de diferença estatisticamente significativa entre os dois grupos.

First-order decay models to describe soil C-CO2 Loss after rotary tillage

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.