Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Estimação de regressões aditivas via backfitting e integração marginal: performance em pequenas amostras

Nesta dissertação realizou-se um experimento de Monte Carlo para re- velar algumas características das distribuições em amostras finitas dos estimadores Backfitting (B) e de Integração Marginal(MI) para uma regressão aditiva bivariada. Está-se particularmente interessado em fornecer alguma evidência de como os diferentes métodos de seleção da janela hn, tais co- mo os métodos plug-in, impactam as propriedades em pequenas amostras dos estimadores. Está-se interessado, também, em fornecer evidência do comportamento de diferentes estimadores de hn relativamente a seqüência ótima de hn que minimiza uma função perda escolhida. O impacto de ignorar a dependência entre os regressores na estimação da janela é tam- bém investigado. Esta é uma prática comum e deve ter impacto sobre o desempenho dos estimadores. Além disso, não há nenhuma rotina atual- mente disponível nos pacotes estatísticos/econométricos para a estimação de regressões aditivas via os métodos de Backfitting e Integração Marginal. É um dos objetivos a criação de rotinas em Gauss para a implementação prática destes estimadores. Por fim, diferentemente do que ocorre atual- mente, quando a utilização dos estimadores-B e MI é feita de maneira completamente ad-hoc, há o objetivo de fornecer a usuários informação que permita uma escolha mais objetiva de qual estimador usar quando se está trabalhando com uma amostra finita.

Nonparametric specification tests for conditional duration models

This paper deals with the testing of autoregressive conditional duration (ACD) models by gauging the distance between the parametric density and hazard rate functions implied by the duration process and their non-parametric estimates. We derive the asymptotic justification using the functional delta method for fixed and gamma kernels, and then investigate the finite-sample properties through Monte Carlo simulations. Although our tests display some size distortion, bootstrapping suffices to correct the size without compromising their excellent power. We show the practical usefulness of such testing procedures for the estimation of intraday volatility patterns.

Nonparametric entropy-based tests of independence between stochastic processes

This paper develops nonparametric tests of independence between two stationary stochastic processes. The testing strategy boils down to gauging the closeness between the joint and the product of the marginal stationary densities. For that purpose, I take advantage of a generalized entropic measure so as to build a class of nonparametric tests of independence. Asymptotic normality and local power are derived using the functional delta method for kernels, whereas finite sample properties are investigated through Monte Carlo simulations.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Non- parametric specification tests for conditional duration models

This paper deals with the estimation and testing of conditional duration models by looking at the density and baseline hazard rate functions. More precisely, we foeus on the distance between the parametric density (or hazard rate) function implied by the duration process and its non-parametric estimate. Asymptotic justification is derived using the functional delta method for fixed and gamma kernels, whereas finite sample properties are investigated through Monte Carlo simulations. Finally, we show the practical usefulness of such testing procedures by carrying out an empirical assessment of whether autoregressive conditional duration models are appropriate to oIs for modelling price durations of stocks traded at the New York Stock Exchange.

Recognition of altered segments in Brazilian Sign Language

The purpose of this study was to verify discriminative control by segments of signs in adolescents with deafness who use Brazilian Sign Language (BSL). Four adolescent with bilateral deafness, with 3 years of BSL teaching, saw a video presenting a children's tale in BSL. After showing accurate understanding of the story, participants saw another video of the same story with 12 signs altered in one of their segments (hand configuration, place of articulation, or movement). They apparently did not detect the alterations. However, when the signs were presented in isolation in a matching-to-sample test, they virtually always selected the picture corresponding to the unaltered signs. Three participants selected an unfamiliar picture in 50% or more trials with an altered sign as a sample, showing that they could detect the majority of the altered signs.

Reliability evaluation of distribution systems considering automatic reclosers

This paper presents a new methodology to evaluate in a predictive way the reliability of distribution systems, considering the impact of automatic recloser switches. The developed algorithm is based on state enumeration techniques with Markovian models and on the minimal cut set theory. Some computational aspects related with the implementation of the proposed algorithm in typical distribution networks are also discussed. The description of the proposed approach is carried out using a sample test system. The results obtained with a typical configuration of a Brazilian system (EDP Bandeirante Energia S.A.) are presented and discussed.

A mechatronic system applied to wear particle analysis

This paper describes the development of a mechatronic system for a predictive maintenance grounded on wear particle analysis. The reckoning of wear particles containing in lubricating industrial oils brings the image acquisition system into being. The ISO 4406:1999 standard is a guide to establish the counting and evaluation processes of particles. The system applied to the acquisition and analysis of the data consists of a digital camera, a monocular microscope and an oil filtering system. A computational program was developed with the application of Visual Microsoft C++ in a way to detain the oil sample image from the microscope slide to the computer screen. Quantitative analyses of the wear debris particles bulk are exploited applying a graphical interface that was developed to render the image processing of the sample test. The implemented system has a reachable cost thus it can be applied for schooling goals and for bolstering laboratories of minor industries and medium size companies.

Desenvolvimento de pino intra-radicular, resistência à fratura e análise da distribuição de tensões através do método dos elementos finitos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influência do tipo de ponteira utilizada na fotoativação sobre o grau de conversão, dureza e resistência á compressão de resinas compostas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Efeito do treino de discriminação simples sobre o repertório de pareamento ao modelo por identidade de um macaco-prego (Cebus apella)

Foi investigado como o treino de Reversões Repetidas de Discriminações Simples (RRDS) poderia facilitar a aquisição do desempenho de pareamento ao modelo por identidade (IDMTS). Utilizou-se um macaco-prego macho, com pouco mais de um ano de idade, com treino prévio de pareamento ao modelo por identidade. Foram utilizados dois conjuntos: A com quatro e B com cinco estímulos. A tarefa de IDMTS com atraso zero e com quatro escolhas com o Conjunto A foi usada como linha de base, utilizando razão três nas respostas de toque. Foi feito treino de RRDS com cada um dos pares possíveis do conjunto B. Depois das RRDS, testou-se o par em tentativas de pareamento ao modelo misturadas à linha de base. O desempenho na discriminação simples entre os pares de estímulos do conjunto B variou quanto ao número de sessões para atingir o critério de seis corretas em um máximo de 12 tentativas. No IDMTS com cada par do conjunto B, o critério foi atingido em 9 sessões na primeira etapa e 12 sessões na segunda etapa. Porém, houve erros nas tentativas de IDMTS na primeira fase da terceira etapa, quando os estímulos do conjunto B compuseram tentativas com quatro escolhas. O aumento de acertos no IDMTS nas tentativas do conjunto B ocorreu com a substituição de um dos estímulos, mostrando problemas de discriminabilidade entre o estímulo eliminado e os demais. Discute-se um possível conflito entre as topografias de controle selecionadas pelas RRDS.

Recovery Tests of an Hybrid Current Limiter Composed of a SFCL in Parallel to an Air-Core Power Reactor

The resistive-type superconducting fault current limiters (RSFCL) prototypes using YBCO-coated conductors have shown current limitation for medium voltage class applications for acting time up to 80 ms. By connecting an air-core reactor in parallel with the RSFCL, thus making an hybrid current limiter, one can extend the acting time for up to 1 s. In this work, we report the performance of a hybrid current limiter subjected to an AC peak fault current of 2 kA during 1 s for which within the first 80 ms the SFCL limits the current concurrently with the air-core reactor, and for the remaining 920 ms, only the air-core reactor limits the current. In order to evaluate the actual conditions for subsequent reconnection of RSFCL to the power grid, the hybrid fault current limiter was tested varying the time interval for recovery from 900 ms and 1.2 s, followed again by the concurrent operation of the hybrid limiter during 1 s (SFCL during 80 ms). From this evaluation test, the recovery time can be measured and compared using the voltage peak generated in superconducting module from the first and second fault test. The recovery time was also determined through the pulsed current method (PCM) on short-length sample test. The results showed that the fault current was limited from 1.9 kA down to 514 A after 1 cycle of 60 Hz frequency, with recovery time lower than 1.2 s for two subsequent fault current tests.

THE LOG-BURR XII REGRESSION MODEL FOR GROUPED SURVIVAL DATA

The log-Burr XII regression model for grouped survival data is evaluated in the presence of many ties. The methodology for grouped survival data is based on life tables, where the times are grouped in k intervals, and we fit discrete lifetime regression models to the data. The model parameters are estimated by maximum likelihood and jackknife methods. To detect influential observations in the proposed model, diagnostic measures based on case deletion, so-called global influence, and influence measures based on small perturbations in the data or in the model, referred to as local influence, are used. In addition to these measures, the total local influence and influential estimates are also used. We conduct Monte Carlo simulation studies to assess the finite sample behavior of the maximum likelihood estimators of the proposed model for grouped survival. A real data set is analyzed using a regression model for grouped data.

Arbitrage-Free Conditions and Hedging Strategies for Markets with Penalty Costs on Short Positions

We consider a discrete-time financial model in a general sample space with penalty costs on short positions. We consider a friction market closely related to the standard one except that withdrawals from the portfolio value proportional to short positions are made. We provide necessary and sufficient conditions for the nonexistence of arbitrages in this situation and for a self-financing strategy to replicate a contingent claim. For the finite-sample space case, this result leads to an explicit and constructive procedure for obtaining perfect hedging strategies.