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Monitorização, modelação e melhoria de processos químicos : abordagem multiescala baseada em dados; Data-driven multiscale monitoring, modelling and improvement of chemical processes

Reis, Marco Paulo Seabra
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Tese de Doutorado
Português
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Processes going on in modern chemical processing plants are typically very complex, and this complexity is also present in collected data, which contain the cumulative effect of many underlying phenomena and disturbances, presenting different patterns in the time/frequency domain. Such characteristics motivate the development and application of data-driven multiscale approaches to process analysis, with the ability of selectively analyzing the information contained at different scales, but, even in these cases, there is a number of additional complicating features that can make the analysis not being completely successful. Missing and multirate data structures are two representatives of the difficulties that can be found, to which we can add multiresolution data structures, among others. On the other hand, some additional requisites should be considered when performing such an analysis, in particular the incorporation of all available knowledge about data, namely data uncertainty information. In this context, this thesis addresses the problem of developing frameworks that are able to perform the required multiscale decomposition analysis while coping with the complex features present in industrial data and, simultaneously, considering measurement uncertainty information. These frameworks are proven to be useful in conducting data analysis in these circumstances...

Bias correction in a multivariate normal regression model with general parameterization

PATRIOTA, Alexandre G.; LEMONTE, Artur J.
Fonte: ELSEVIER SCIENCE BV Publicador: ELSEVIER SCIENCE BV
Tipo: Artigo de Revista Científica
Português
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This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); FAPESP (Brazil)

Modelos de regressão multivariada; Multivariate regression models

Nogueira, Fabio Esteves
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/04/2007 Português
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Os modelos de Regressão Linear Multivariada apesar de serem pouco utilizados são muito úteis pois, dentre outras vantagens, permitem a construçãoo de modelos considerando estruturas de correlação entre medidas tomadas na mesma ou em distintas unidades amostrais. Neste trabalho apresentamos os métodos de estimação dos parâmetros, medidas para análise de diagnóstico, procedimentos de seleção de variáveis e uma aplicação dessa técnica de modelagem em um conjunto de dados reais.; Multivariate Linear Regression Models are not frequently used although they are very useful. Working with this kind of model, it is possible to analyse correlated response variables jointly. In this dissertation, we dedicate initially to describe the inferencial methods in Multivariate Linear Regression models. Further, we describe some measures of diagnostics and methods of variable selection in this model. Finally, some of the describe procedures are applied in a real data set.

Análise da precificação de imóveis na cidade do Rio de Janeiro utilizando Modelagem Hedônica e os efeitos da autocorrelação espacial

Rodrigues, Sérgio da Silva
Fonte: Fundação Getúlio Vargas Publicador: Fundação Getúlio Vargas
Tipo: Dissertação
Português
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A escolha da cidade do Rio de Janeiro como sede de grandes eventos esportivos mundiais, a Copa do Mundo de Futebol de 2014 e os Jogos Olímpicos de 2016, colocou-a no centro de investimentos em infraestrutura, mobilidade urbana e segurança pública, com consequente impacto no mercado imobiliário, tanto de novos lançamentos de empreendimentos, quanto na revenda de imóveis usados. Acredita-se que o preço de um imóvel dependa de uma relação entre suas características estruturais como quantidade de quartos, suítes, vagas de garagem, presença de varanda, tal como sua localização, proximidade com centros de trabalho, entretenimento e áreas valorizadas ou degradadas. Uma das técnicas para avaliar a contribuição dessas características para a formação do preço do imóvel, conhecido na Econométrica como Modelagem Hedônica de Preços, é uma aplicação de regressão linear multivariada onde a variável dependente é o preço e as variáveis independentes, as respectivas características que deseja-se modelar. A utilização da regressão linear implica em observar premissas que devem ser atendidas para a confiabilidade dos resultados a serem analisados, tais como independência e homoscedasticidade dos resíduos e não colinearidade entre as variáveis independentes. O presente trabalho objetiva aplicar a modelagem hedônica de preços para imóveis localizados na cidade do Rio de Janeiro em um modelo de regressão linear multivariada...

Local Polynomial Estimation of Heteroscedasticity in a Multivariate Linear Regression Model and Its Applications in Economics

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 17/09/2012 Português
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Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

Zare Abyaneh, Hamid
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 23/01/2014 Português
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This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also...

Paralelização de algoritmos APS e Firefly para seleção de variáveis em problemas de calibração multivariada; Parallelization of APF and Firefly algorithms for variable selection in multivariate calibration problems

Paula, Lauro Cássio Martins de
Fonte: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG) Publicador: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG)
Tipo: Dissertação Formato: application/pdf
Português
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The problem of variable selection is the selection of attributes for a given sample that best contribute to the prediction of the property of interest. Traditional algorithms as Successive Projections Algorithm (APS) have been quite used for variable selection in multivariate calibration problems. Among the bio-inspired algorithms, we note that the Firefly Algorithm (AF) is a newly proposed method with potential application in several real world problems such as variable selection problem. The main drawback of these tasks lies in them computation burden, as they grow with the number of variables available. The recent improvements of Graphics Processing Units (GPU) provides to the algorithms a powerful processing platform. Thus, the use of GPUs often becomes necessary to reduce the computation time of the algorithms. In this context, this work proposes a GPU-based AF (AF-RLM) for variable selection using multiple linear regression models (RLM). Furthermore, we present two APS implementations, one using RLM (APSRLM) and the other sequential regressions (APS-RS). Such implementations are aimed at improving the computational efficiency of the algorithms. The advantages of the parallel implementations are demonstrated in an example involving a large number of variables. In such example...

Refining the prediction of potential malt fermentability by including an assessment of limit dextrinase thermostability and additional measures of malt modification, using two different methods for multivariate model development

Evans, D.; Dambergs, R.; Ratkowsky, D.; Li, C.; Harasymow, S.; Roumeliotis, S.; Eglinton, J.
Fonte: Inst Brewing Publicador: Inst Brewing
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
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Prediction of malt fermentability (apparent attenuation limit –AAL) by measurement of the diastatic power enzymes (DPE), α-amylase, total limit dextrinase, total β-amylase, β-amylase thermostability, and the Kolbach index (KI or free amino nitrogen – FAN) is superior to the conventional use of diastatic power (DP) alone. The thermostability of β-amylase is known to be an important factor in determining fermentability, thus the thermostability of the other relatively thermolabile enzyme, limit dextrinase, was investigated to determine if it was also useful in predicting fermentability. To facilitate this aim, methods were developed for a rapid and cost efficient assay of both β-amylase and limit dextrinase thermostability. Internationally important Australian and international malting varieties were compared for their total limit dextrinase and β-amylase activity and thermostability. Interestingly, the level of limit dextrinase thermostability was observed to be inversely correlated with total limit dextrinase activity. The prediction of malt fermentability was achieved by both forward step-wise multi-linear regression (MLR) and the partial least squares (PLS) multivariate model development methods. Both methods produced similar identifications of the parameters predicting wort fermentability at similar levels of predictive power. Both models were substantially better at predicting fermentability than the traditional use of DP on its own. The emphasis of this study was on the identification of predictive factors that can be consistently used in models to predict fermentability...

Bayesian predictive inference for multivariate simple regression model with matrix-T error

Rahman, Azizur
Fonte: Pioneer Scientific Publisher Publicador: Pioneer Scientific Publisher
Tipo: Artigo de Revista Científica
Publicado em //2011 Português
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The Bayesian methodology is used in this paper to derive the prediction distribution of future responses matrix for multivariate simple linear model with matrix-T error. Results reveal that the prediction distribution of future responses matrix is a matrix-T distribution with appropriate location, scale and shape parameters. The prediction distribution depends on the realized responses only through the sample regression matrix and the sample residual sum of squares and products matrix. The study model is robust and the Bayesian method is competitive with other statistical methods in the field of predictive inference. Some applications of predictive inference have also been illustrated.; http://www.pspchv.com/content_1_PJTAS_2.html; Azizur Rahman

Multivariate analysis of the effect of source of supply and carrier on shipping times for issue priority group one (IPG-1) requisitions

Schorn, Brian
Fonte: Monterey, California. Naval Postgraduate School Publicador: Monterey, California. Naval Postgraduate School
Tipo: Tese de Doutorado Formato: xviii, 95 p. : ill. ;
Português
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Approved for public release; distribution in unlimited.; The objective of this thesis is to examine the effects of source of supply and carrier on shipping times of high-priority requisitions to primary destinations of Navy units in the Pacific Theater and Persian Gulf. Our focus was primarily on determining whether source of supply, carrier, and the interaction of these two factors, have an effect on shipping times of highpriority requisitions. "Source of supply" refers to Department of Defense supply depots and "carrier" refers to shippers, such as Federal Expressʼ and DHL Worldwide Expressʼ. This study uses ordinary least square (OLS) linear models, generalized linear models (GLM's) and nonparametric methods to explore the structure of the historical requisition datasets. OLS linear models were found to be inadequate, but both the GLM's and nonparametric tests proved to be valid and yielded results from which inferences could be made. According to the GLM's and nonparametric tests, source of supply has a statistically significant effect on shipping times of high-priority requisitions, but carrier does not. The GLM's also indicated that there is no significant interaction between source of supply and carrier.; Lieutenant Commander...

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

DUFOUR, Jean-Marie; KHALAF, Lynda; BEAULIEU, Marie-Claude
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 219916 bytes; application/pdf
Português
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In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates...

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

DUFOUR, Jean-Marie; KHALAF, Lynda; BEAULIEU, Marie-Claude
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 225374 bytes; application/pdf
Português
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We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.; Dans cet article, nous proposons des tests sur la forme de la distribution des erreurs dans un modèle de régression linéaire multivarié (RLM). Les tests que nous développons sont fonction des résidus obtenus par moindres carrés multivariés...

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

DUFOUR, Jean-Marie; KHALAF, Lynda
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 762828 bytes; application/pdf
Português
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In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g....

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

BEAULIEU, Marie-Claude; DUFOUR, Jean-Marie; KHALAF, Lynda
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Artigo de Revista Científica Formato: 204421 bytes; application/pdf
Português
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In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Convex Optimization Methods for Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression

Lu, Zhaosong; Monteiro, Renato D. C.; Yuan, Ming
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 04/04/2009 Português
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In this paper, we study convex optimization methods for computing the trace norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection (FES) method, recently proposed by Yuan et al. [22], conducts parameter estimation and factor selection simultaneously and have been shown to enjoy nice properties in both large and finite samples. To compute the estimates, however, can be very challenging in practice because of the high dimensionality and the trace norm constraint. In this paper, we explore a variant of Nesterov's smooth method [20] and interior point methods for computing the penalized least squares estimate. The performance of these methods is then compared using a set of randomly generated instances. We show that the variant of Nesterov's smooth method [20] generally outperforms the interior point method implemented in SDPT3 version 4.0 (beta) [19] substantially . Moreover, the former method is much more memory efficient.; Comment: 27 pages

A Gibbs Sampler for Multivariate Linear Regression

Mantz, Adam B.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/09/2015 Português
Relevância na Pesquisa
57.989175%
Kelly (2007, hereafter K07) described an efficient algorithm, using Gibbs sampling, for performing linear regression in the fairly general case where non-zero measurement errors exist for both the covariates and response variables, where these measurements may be correlated (for the same data point), where the response variable is affected by intrinsic scatter in addition to measurement error, and where the prior distribution of covariates is modeled by a flexible mixture of Gaussians rather than assumed to be uniform. Here I extend the K07 algorithm in two ways. First, the procedure is generalized to the case of multiple response variables. Second, I describe how to model the prior distribution of covariates using a Dirichlet process, which can be thought of as a Gaussian mixture where the number of mixture components is learned from the data. I present an example of multivariate regression using the extended algorithm, namely fitting scaling relations of the gas mass, temperature, and luminosity of dynamically relaxed galaxy clusters as a function of their mass and redshift. An implementation of the Gibbs sampler in the R language, called LRGS, is provided.; Comment: 9 pages, 5 figures, 2 tables

On the Data Augmentation Algorithm for Bayesian Multivariate Linear Regression with Non-Gaussian Errors

Qin, Qian; Hobert, James P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/12/2015 Português
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Let $\pi$ denote the intractable posterior density that results when the likelihood from a multivariate linear regression model with errors from a scale mixture of normals is combined with the standard non-informative prior. There is a simple data augmentation algorithm (based on latent data from the mixing density) that can be used to explore $\pi$. Hobert et al. (2015) [arXiv:1506.03113v1] recently performed a convergence rate analysis of the Markov chain underlying this MCMC algorithm in the special case where the regression model is univariate. These authors provide simple sufficient conditions (on the mixing density) for geometric ergodicity of the Markov chain. In this note, we extend Hobert et al.'s (2015) result to the multivariate case.; Comment: 10 pages. arXiv admin note: text overlap with arXiv:1506.03113

Metodología para el análisis de las transformaciones paisajísticas de áreas rurales mediterráneas. Evolución, causas y consecuencias en el caso del Alto Ampurdán (Noreste de Cataluña)

Serra Ruíz, Pere; Saurí i Pujol, David
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2005 Português
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El artículo presenta una metodología aplicable al análisis de las dinámicas paisajísticas de las áreas rurales mediterráneas a través del uso conjunto de cuatro herramientas: historia, teledetección con imágenes de satélite, regresión multivariante e índices paisajísticos. El primer proceso consiste en el análisis histórico cuyo objetivo es comprender como la acción humana ha transformado el paisaje a través de los siglos. El segundo es el uso de la teledetección y de los sistemas de información geográfica para la obtención de los mapas de cubiertas y usos del suelo de las últimas décadas. A causa de los sensores usados (Multispectral Scanner (MSS) y Thematic Mapper (TM)) se han diferenciado dos subperiodos: 1977-1993, analizado a través de imágenes MSS, y 1991-1997, a través de imágenes TM. En el tercer paso, se analizan las fuerzas inductoras de los cambios a través de la aplicación de la regresión lineal y de la regresión logística multivariante. Finalmente, se cuantifica la evolución paisajística a través de diversos índices propuestos por la Ecología del Paisaje; The paper presents a methodology for analysing the landscape dynamics of Mediterranean rural areas using four tools: historical accounts...

Statistical inference on linear and partly linear regression with spatial dependence: parametric and nonparametric approaches

Thawornkaiwong, Supachoke
Fonte: London School of Economics and Political Science Thesis Publicador: London School of Economics and Political Science Thesis
Tipo: Thesis; NonPeerReviewed Formato: application/pdf
Publicado em /08/2012 Português
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The typical assumption made in regression analysis with cross-sectional data is that of independent observations. However, this assumption can be questionable in some economic applications where spatial dependence of observations may arise, for example, from local shocks in an economy, interaction among economic agents and spillovers. The main focus of this thesis is on regression models under three di§erent models of spatial dependence. First, a multivariate linear regression model with the disturbances following the Spatial Autoregressive process is considered. It is shown that the Gaussian pseudo-maximum likelihood estimate of the regression and the spatial autoregressive parameters can be root-n-consistent under strong spatial dependence or explosive variances, given that they are not too strong, without making restrictive assumptions on the parameter space. To achieve e¢ ciency improvement, adaptive estimation, in the sense of Stein (1956), is also discussed where the unknown score function is nonparametrically estimated by power series estimation. A large section is devoted to an extension of power series estimation for random variables with unbounded supports. Second, linear and semiparametric partly linear regression models with the disturbances following a generalized linear process for triangular arrays proposed by Robinson (2011) are considered. It is shown that instrumental variables estimates of the unknown slope parameters can be root-n-consistent even under some strong spatial dependence. A simple nonparametric estimate of the asymptotic variance matrix of the slope parameters is proposed. An empirical illustration of the estimation technique is also conducted. Finally...

DETERMINING INDICATORS OF URBAN HOUSEHOLD WATER CONSUMPTION THROUGH MULTIVARIATE STATISTICAL TECHNIQUES (doi: 10.4090/juee.2010.v4n2.074080)

Lins, Gledsneli Maria Lima; Cruz, Walter Santa; Vieira, Zédna Mara Castro Lucena; Neto, Francisco de Assis Costa; Miranda, Érico Alberto Albuquerque
Fonte: JUEE Press Publicador: JUEE Press
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; Formato: application/pdf
Publicado em 13/06/2011 Português
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Water has a decisive influence on populations’ life quality – specifically in areas like urban supply, drainage, and effluents treatment – due to its sound impact over public health. Water rational use constitutes the greatest challenge faced by water demand management, mainly with regard to urban household water consumption. This makes it important to develop researches to assist water managers and public policy-makers in planning and formulating water demand measures which may allow urban water rational use to be met. This work utilized the multivariate techniques Factor Analysis and Multiple Linear Regression Analysis – in order to determine the participation level of socioeconomic and climatic variables in monthly urban household consumption changes – applying them to two districts of Campina Grande city (State of Paraíba, Brazil). The districts were chosen based on socioeconomic criterion (income level) so as to evaluate their water consumer’s behavior. A 9-year monthly data series (from year 2000 up to 2008) was utilized, comprising family income, water tariff, and quantity of household connections (economies) – as socioeconomic variables – and average temperature and precipitation, as climatic variables. For both the selected districts of Campina Grande city...