Bias-corrected estimators for dispersion models with dispersion covariates

In this paper we discuss bias-corrected estimators for the regression and the dispersion parameters in an extended class of dispersion models (Jorgensen, 1997b). This class extends the regular dispersion models by letting the dispersion parameter vary throughout the observations, and contains the dispersion models as particular case. General formulae for the O(n(-1)) bias are obtained explicitly in dispersion models with dispersion covariates, which generalize previous results obtained by Botter and Cordeiro (1998), Cordeiro and McCullagh (1991), Cordeiro and Vasconcellos (1999), and Paula (1992). The practical use of the formulae is that we can derive closed-form expressions for the O(n(-1)) biases of the maximum likelihood estimators of the regression and dispersion parameters when the information matrix has a closed-form. Various expressions for the O(n(-1)) biases are given for special models. The formulae have advantages for numerical purposes because they require only a supplementary weighted linear regression. We also compare these bias-corrected estimators with two different estimators which are also bias-free to order O(n(-1)) that are based on bootstrap methods. These estimators are compared by simulation. (C) 2011 Elsevier B.V. All rights reserved.

Birnbaum-Saunders nonlinear regression models

We introduce, for the first time, a new class of Birnbaum-Saunders nonlinear regression models potentially useful in lifetime data analysis. The class generalizes the regression model described by Rieck and Nedelman [Rieck, J.R., Nedelman, J.R., 1991. A log-linear model for the Birnbaum-Saunders distribution. Technometrics 33, 51-60]. We discuss maximum-likelihood estimation for the parameters of the model, and derive closed-form expressions for the second-order biases of these estimates. Our formulae are easily computed as ordinary linear regressions and are then used to define bias corrected maximum-likelihood estimates. Some simulation results show that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. Two empirical applications are analysed and discussed. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

Deep bed filtration : mathematical models and observations

Numerous mathematical models have been developed to evaluate both initial and transient stage removal efficiency of deep bed filters. Microscopic models either using trajectory analysis or convective diffusion equations were used to compute the initial removal efficiency. These models predicted the removal efficiency under favorable filtration conditions quantitatively, but failed to predict the removal efficiency under unfavorable conditions. They underestimated the removal efficiency under unfavorable conditions. Thus, semi-empirical formulations were developed to compute initial removal efficiencies under unfavorable conditions. Also, correction for the adhesion of particles onto filter grains improved the results obtained for removal efficiency from the trajectory analysis. Macroscopic models were used to predict the transient stage removal efficiency of deep bed filters. The O’Melia and Ali1 model assumed that the particle removal is due to filter grains as well as the particles that are already deposited onto the filter grain. Thus, semi-empirical models were used to predict the ripening of filtration. Several modifications were made to the model developed by O’Melia and Ali to predict the deterioration of particle removal during the transient stages of filtration. Models considering the removal of particles under favorable conditions and the accumulation of charges on the filter grains during the transient stages were also developed. This article evaluates those models and their applicability under different operating conditions of filtration.

A panel vector error correction approach to forecasting demand in regional construction markets

Reliable forecasting as to the level of aggregate demand for construction is of vital importance to developers, builders and policymakers. Previous construction demand forecasting studies mainly focused on temporal estimating using national aggregate data. The construction market can be better represented by a group of interconnected regions or local markets rather than a national aggregate, and yet regional forecasting techniques have rarely been applied. Furthermore, limited research has applied regional variations in construction markets to construction demand modelling and forecasting. A new comprehensive method is used, a panel vector error correction approach, to forecast regional construction demand using Australia’s state-level data. The links between regional construction demand and general economic indicators are investigated by panel cointegration and causality analysis. The empirical results suggest that both long-run and causal links are found between regional construction demand and construction price, state income, population, unemployment rates and interest rates. The panel vector error correction model can provide reliable and robust forecasting with less than 10% of the mean absolute percentage error for a medium-term trend of regional construction demand and outperforms the conventional forecasting models (panel multiple regression and time series multiple regression model). The key macroeconomic factors of construction demand variations across regions in Australia are also presented. The findings and robust econometric techniques used are valuable to construction economists in examining future construction markets at a regional level.

Evaluating the existence of structural change in the brazilian term structure of interest: evidence based on cointegration models with structural break

This paper investigates whether there is evidence of structural change in the Brazilian term structure of interest rates. Multivariate cointegration techniques are used to verify this evidence. Two econometrics models are estimated. The rst one is a Vector Autoregressive Model with Error Correction Mechanism (VECM) with smooth transition in the deterministic coe¢ cients (Ripatti and Saikkonen [25]). The second one is a VECM with abrupt structural change formulated by Hansen [13]. Two datasets were analysed. The rst one contains a nominal interest rate with maturity up to three years. The second data set focuses on maturity up to one year. The rst data set focuses on a sample period from 1995 to 2010 and the second from 1998 to 2010. The frequency is monthly. The estimated models suggest the existence of structural change in the Brazilian term structure. It was possible to document the existence of multiple regimes using both techniques for both databases. The risk premium for di¤erent spreads varied considerably during the earliest period of both samples and seemed to converge to stable and lower values at the end of the sample period. Long-term risk premiums seemed to converge to inter-national standards, although the Brazilian term structure is still subject to liquidity problems for longer maturities.

Does mixed frequency vector error correction model add relevant information to exchange misalignment calculus? Evidence for United States

Real exchange rate is an important macroeconomic price in the economy and a ects economic activity, interest rates, domestic prices, trade and investiments ows among other variables. Methodologies have been developed in empirical exchange rate misalignment studies to evaluate whether a real e ective exchange is overvalued or undervalued. There is a vast body of literature on the determinants of long-term real exchange rates and on empirical strategies to implement the equilibrium norms obtained from theoretical models. This study seeks to contribute to this literature by showing that it is possible to calculate the misalignment from a mixed ointegrated vector error correction framework. An empirical exercise using United States' real exchange rate data is performed. The results suggest that the model with mixed frequency data is preferred to the models with same frequency variables

Short-term temporal changes of bare soil CO2 fluxes after tillage described by first-order decay models

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

Combinatorial identities and quantum state densities of supersymmetric sigma models on N-folds

There is a remarkable connection between the number of quantum states of conformal theories and the sequence of dimensions of Lie algebras. In this paper, we explore this connection by computing the asymptotic expansion of the elliptic genus and the microscopic entropy of black holes associated with (supersymmetric) sigma models. The new features of these results are the appearance of correct prefactors in the state density expansion and in the coefficient of the logarithmic correction to the entropy.

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

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

THE ALGEBRA OF NONLOCAL CHARGES IN NONLINEAR SIGMA-MODELS

We obtain the exact classical algebra obeyed by the conserved non-local charges in bosonic non-linear sigma models. Part of the computation is specialized for a symmetry group O(N). As it turns out the algebra corresponds to a cubic deformation of the Kac-Moody algebra. We generalize the results for the presence of a Wess-Zumino term. The algebra is very similar to the previous one, now containing a calculable correction of order one unit lower. The relation with Yangians and the role of the results in the context of Lie-Poisson algebras are also discussed.

Generating VRS data using atmospheric models: How far can we go?

Nowadays, with the expansion of the reference stations networks, several positioning techniques have been developed and/or improved. Among them, the VRS (Virtual Reference Station) concept has been very used. In this paper the goal is to generate VRS data in a modified technique. In the proposed methodology the DD (double difference) ambiguities are not computed. The network correction terms are obtained using only atmospheric (ionospheric and tropospheric) models. In order to carry out the experiments it was used data of five reference stations from the GPS Active Network of West of São Paulo State and an extra station. To evaluate the VRS data quality it was used three different strategies: PPP (Precise Point Positioning) and Relative Positioning in static and kinematic modes, and DGPS (Differential GPS). Furthermore, the VRS data were generated in the position of a real reference station. The results provided by the VRS data agree quite well with those of the real file data.

Effects of the Eccentricity of a Perturbing Third Body on the Orbital Correction Maneuvers of a Spacecraft

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Thermochemical equilibrium modeling of biomass downdraft gasifier: Stoichiometric models

The aim of this work is to develop stoichiometric equilibrium models that permit the study of parameters effect in the gasification process of a particular feedstock. In total four models were tested in order to determine the syngas composition. One of these four models, called M2, was based on the theoretical equilibrium constants modified by two correction factors determined using published experimental data. The other two models, M3 and M4 were based in correlations, while model M4 was based in correlations to determine the equilibrium constants, model M3 was based in correlations that relate the H-2, CO and CO2 content on the synthesis gas. Model M2 proved to be the more accurate and versatile among these four models, and also showed better results than some previously published models. Also a case study for the gasification of a blend of hardwood chips and glycerol at 80% and 20% respectively, was performed considering equivalence ratios form 0.3 to 0.5, moisture contents from 0%-20% and oxygen percentages in the gasification agent of 100%, 60% and 21%. (C) 2013 Elsevier Ltd. All rights reserved.

Three-dimensional dental arch changes of patients submitted to orthodontic-surgical treatment for correction of Class II malocclusion

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of hourly, daily and monthly mean diffuse radiation based on MEO shadowring correction

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