917 resultados para complex nonlinear least squares


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Dividends and their distribution decisions, being a component of the compensation of investors are a constant financial worry within companies, thus revealing one of the themes highlighted in the context of the financial literature. Study will address the factors determining the dividend policy practiced by companies listed in the Portuguese stock market. The latter will be 47 non-financial companies listed on the Euronext Lisbon during 2009 until 2011. The two samples that have been investigated include the representative of the majority of non-financial companies listed on Euronext Lisbon and the other financial companies members of the PSI 20. The methodology adopted is one of the ordinary least squares regression and the amount of dividends per share distributed was used in determining the dependent variable. In relation to the independent variables, six explanatory factors were chosen. These include profitability, stability of dividend policy, size, growth, risk and investment opportunities. The conclusion suggests that the most important factors to explain the amount of dividends distributed are profitability and stability of dividend policy. There after, growth and risk factors, as well as factors that explain the amount of dividends distributed are also relevant. The remaining variables obtained were insufficient evidence pointing to a significant effect in explaining the dividend policy of Portuguese companies in the sample. The conclusion also states that differences exist in the importance of the explanatory factors to the amount of dividends distributed between the study samples, given the differentiation of dividend policies, followed by companies from each group analyzed.

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The main objective of this paper is to analyse the effect of firms’ performance indicators in explaining the price of stocks in the Portuguese capital market, using a fundamental analysis. In the empirical setting, firms’ performance indicators are gathered into two groups: (1) economic and financial indicators and (2) stock market indicators. Using a sample of 38 firms quoted at Euronext Lisbon, estimates are obtained trough an Ordinary Least Squares (OLS) model and report to December, 31 2007. Results suggest that performance indicators are able to explain the firms’ stock market price. There is a significant positive impact of sales growth and of payout ratio, while we find a statistically significant negative effect of the firm’s financial autonomy on the stock market price for the majority of firms quoted at Euronext Lisbon.

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Este trabalho procura analisar as inter-relações entre a inovatividade, o envolvimento, a atitude dentro do modelo Theory of Planned Behavior (TPB) decomposto desenvolvido na psicologia social, e a experiência com a Internet com o processo de adoção da compra pela internet. Foi elaborado um modelo integrativo que possibilitasse explicar a relação entre esses fatores e a compra pela internet, e foi desenvolvida uma pesquisa de campo considerando uma amostra não probabilística de estudantes. Foi utilizado o método multivariado de modelagem de equações estruturais, aplicado por meio da técnica Partial Least Squares (PLS) para a verificação, explicação e comparação das relações entre os construtos. Os resultados mostram que a intenção da compra pela internet é diretamente influenciada pela atitude e pela inovatividade, e a atitude é influenciada pelo envolvimento. Não foi encontrada relação entre a experiência com a internet e a compra pela internet.

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The portfolio generating the iTraxx EUR index is modeled by coupled Markov chains. Each of the industries of the portfolio evolves according to its own Markov transition matrix. Using a variant of the method of moments, the model parameters are estimated from a data set of Standard and Poor's. Swap spreads are evaluated by Monte-Carlo simulations. Along with an actuarially fair spread, at least squares spread is considered.

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Copyright: © 2014 Aranda et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Environmental pollution continues to be an emerging study field, as there are thousands of anthropogenic compounds mixed in the environment whose possible mechanisms of toxicity and physiological outcomes are of great concern. Developing methods to access and prioritize the screening of these compounds at trace levels in order to support regulatory efforts is, therefore, very important. A methodology based on solid phase extraction followed by derivatization and gas chromatography-mass spectrometry analysis was developed for the assessment of four endocrine disrupting compounds (EDCs) in water matrices: bisphenol A, estrone, 17b-estradiol and 17a-ethinylestradiol. The study was performed, simultaneously, by two different laboratories in order to evaluate the robustness of the method and to increase the quality control over its application in routine analysis. Validation was done according to the International Conference on Harmonisation recommendations and other international guidelines with specifications for the GC-MS methodology. Matrix-induced chromatographic response enhancement was avoided by using matrix-standard calibration solutions and heteroscedasticity has been overtaken by a weighted least squares linear regression model application. Consistent evaluation of key analytical parameters such as extraction efficiency, sensitivity, specificity, linearity, limits of detection and quantification, precision, accuracy and robustness was done in accordance with standards established for acceptance. Finally, the application of the optimized method in the assessment of the selected analytes in environmental samples suggested that it is an expedite methodology for routine analysis of EDC residues in water matrices.

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Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

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In this thesis we implement estimating procedures in order to estimate threshold parameters for the continuous time threshold models driven by stochastic di®erential equations. The ¯rst procedure is based on the EM (expectation-maximization) algorithm applied to the threshold model built from the Brownian motion with drift process. The second procedure mimics one of the fundamental ideas in the estimation of the thresholds in time series context, that is, conditional least squares estimation. We implement this procedure not only for the threshold model built from the Brownian motion with drift process but also for more generic models as the ones built from the geometric Brownian motion or the Ornstein-Uhlenbeck process. Both procedures are implemented for simu- lated data and the least squares estimation procedure is also implemented for real data of daily prices from a set of international funds. The ¯rst fund is the PF-European Sus- tainable Equities-R fund from the Pictet Funds company and the second is the Parvest Europe Dynamic Growth fund from the BNP Paribas company. The data for both funds are daily prices from the year 2004. The last fund to be considered is the Converging Europe Bond fund from the Schroder company and the data are daily prices from the year 2005.

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OBJECTIVE To evaluate the cross-cultural validity of the Demand-Control Questionnaire, comparing the original Swedish questionnaire with the Brazilian version. METHODS We compared data from 362 Swedish and 399 Brazilian health workers. Confirmatory and exploratory factor analyses were performed to test structural validity, using the robust weighted least squares mean and variance-adjusted (WLSMV) estimator. Construct validity, using hypotheses testing, was evaluated through the inspection of the mean score distribution of the scale dimensions according to sociodemographic and social support at work variables. RESULTS The confirmatory and exploratory factor analyses supported the instrument in three dimensions (for Swedish and Brazilians): psychological demands, skill discretion and decision authority. The best-fit model was achieved by including an error correlation between work fast and work intensely (psychological demands) and removing the item repetitive work (skill discretion). Hypotheses testing showed that workers with university degree had higher scores on skill discretion and decision authority and those with high levels of Social Support at Work had lower scores on psychological demands and higher scores on decision authority. CONCLUSIONS The results supported the equivalent dimensional structures across the two culturally different work contexts. Skill discretion and decision authority formed two distinct dimensions and the item repetitive work should be removed.

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Dissertação apresentada para obtenção do Grau de Doutor em Matemática, especialidade de Estatística, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

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Reporter genes are routinely used in every laboratory for molecular and cellular biology for studying heterologous gene expression and general cellular biological mechanisms, such as transfection processes. Although well characterized and broadly implemented, reporter genes present serious limitations, either by involving time-consuming procedures or by presenting possible side effects on the expression of the heterologous gene or even in the general cellular metabolism. Fourier transform mid-infrared (FT-MIR) spectroscopy was evaluated to simultaneously analyze in a rapid (minutes) and high-throughput mode (using 96-wells microplates), the transfection efficiency, and the effect of the transfection process on the host cell biochemical composition and metabolism. Semi-adherent HEK and adherent AGS cell lines, transfected with the plasmid pVAX-GFP using Lipofectamine, were used as model systems. Good partial least squares (PLS) models were built to estimate the transfection efficiency, either considering each cell line independently (R 2 ≥ 0.92; RMSECV ≤ 2 %) or simultaneously considering both cell lines (R 2 = 0.90; RMSECV = 2 %). Additionally, the effect of the transfection process on the HEK cell biochemical and metabolic features could be evaluated directly from the FT-IR spectra. Due to the high sensitivity of the technique, it was also possible to discriminate the effect of the transfection process from the transfection reagent on KEK cells, e.g., by the analysis of spectral biomarkers and biochemical and metabolic features. The present results are far beyond what any reporter gene assay or other specific probe can offer for these purposes.

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The development of biopharmaceutical manufacturing processes presents critical constraints, with the major constraint being that living cells synthesize these molecules, presenting inherent behavior variability due to their high sensitivity to small fluctuations in the cultivation environment. To speed up the development process and to control this critical manufacturing step, it is relevant to develop high-throughput and in situ monitoring techniques, respectively. Here, high-throughput mid-infrared (MIR) spectral analysis of dehydrated cell pellets and in situ near-infrared (NIR) spectral analysis of the whole culture broth were compared to monitor plasmid production in recombinant Escherichia coil cultures. Good partial least squares (PLS) regression models were built, either based on MIR or NIR spectral data, yielding high coefficients of determination (R-2) and low predictive errors (root mean square error, or RMSE) to estimate host cell growth, plasmid production, carbon source consumption (glucose and glycerol), and by-product acetate production and consumption. The predictive errors for biomass, plasmid, glucose, glycerol, and acetate based on MIR data were 0.7 g/L, 9 mg/L, 0.3 g/L, 0.4 g/L, and 0.4 g/L, respectively, whereas for NIR data the predictive errors obtained were 0.4 g/L, 8 mg/L, 0.3 g/L, 0.2 g/L, and 0.4 g/L, respectively. The models obtained are robust as they are valid for cultivations conducted with different media compositions and with different cultivation strategies (batch and fed-batch). Besides being conducted in situ with a sterilized fiber optic probe, NIR spectroscopy allows building PLS models for estimating plasmid, glucose, and acetate that are as accurate as those obtained from the high-throughput MIR setup, and better models for estimating biomass and glycerol, yielding a decrease in 57 and 50% of the RMSE, respectively, compared to the MIR setup. However, MIR spectroscopy could be a valid alternative in the case of optimization protocols, due to possible space constraints or high costs associated with the use of multi-fiber optic probes for multi-bioreactors. In this case, MIR could be conducted in a high-throughput manner, analyzing hundreds of culture samples in a rapid and automatic mode.

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BACKGROUNDWhile the pharmaceutical industry keeps an eye on plasmid DNA production for new generation gene therapies, real-time monitoring techniques for plasmid bioproduction are as yet unavailable. This work shows the possibility of in situ monitoring of plasmid production in Escherichia coli cultures using a near infrared (NIR) fiber optic probe. RESULTSPartial least squares (PLS) regression models based on the NIR spectra were developed for predicting bioprocess critical variables such as the concentrations of biomass, plasmid, carbon sources (glucose and glycerol) and acetate. In order to achieve robust models able to predict the performance of plasmid production processes, independently of the composition of the cultivation medium, cultivation strategy (batch versus fed-batch) and E. coli strain used, three strategies were adopted, using: (i) E. coliDH5 cultures conducted under different media compositions and culture strategies (batch and fed-batch); (ii) engineered E. coli strains, MG1655endArecApgi and MG1655endArecA, grown on the same medium and culture strategy; (iii) diverse E. coli strains, over batch and fed-batch cultivations and using different media compositions. PLS models showed high accuracy for predicting all variables in the three groups of cultures. CONCLUSIONNIR spectroscopy combined with PLS modeling provides a fast, inexpensive and contamination-free technique to accurately monitoring plasmid bioprocesses in real time, independently of the medium composition, cultivation strategy and the E. coli strain used.

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O modelo matemático de um sistema real permite o conhecimento do seu comportamento dinâmico e é geralmente utilizado em problemas de engenharia. Por vezes os parâmetros utilizados pelo modelo são desconhecidos ou imprecisos. O envelhecimento e o desgaste do material são fatores a ter em conta pois podem causar alterações no comportamento do sistema real, podendo ser necessário efetuar uma nova estimação dos seus parâmetros. Para resolver este problema é utilizado o software desenvolvido pela empresa MathWorks, nomeadamente, o Matlab e o Simulink, em conjunto com a plataforma Arduíno cujo Hardware é open-source. A partir de dados obtidos do sistema real será aplicado um Ajuste de curvas (Curve Fitting) pelo Método dos Mínimos Quadrados de forma a aproximar o modelo simulado ao modelo do sistema real. O sistema desenvolvido permite a obtenção de novos valores dos parâmetros, de uma forma simples e eficaz, com vista a uma melhor aproximação do sistema real em estudo. A solução encontrada é validada com recurso a diferentes sinais de entrada aplicados ao sistema e os seus resultados comparados com os resultados do novo modelo obtido. O desempenho da solução encontrada é avaliado através do método das somas quadráticas dos erros entre resultados obtidos através de simulação e resultados obtidos experimentalmente do sistema real.