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Metodos de subespaços para identificação de sistemas : propostas de alterações, implementações e avaliações; Subspace methods for systems identification : proposals of alterations, implementations and evaluations

David Giraldo Clavijo
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 14/11/2008 PT
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65.86%
Este estudo apresenta os fundamentos teóricos para modelagem de dados multivariáveis no espaço de estado através de Métodos de Subespaços para Identificação de sistemas lineares invariantes no tempo, discretos no tempo. O trabalho contém alguns conceitos básicos de sistemas dinâmicos, um pouco da história e os elementos de identificação de sistemas, modelos no espaço de estado e modelos estendidos no espaço de estado. Dois Métodos de Subespaços são analisados e tratados, o Multivariable Output-Error State-sPace (MOESP) e o Numerical algorithm for Subspace State-Space System IDentification (N4SID). Modificações nos seus algoritmos são propostas e implementadas. Experimentos com benchmarks são realizados para exemplificar o procedimento de identificação por subespaços e para avaliar os algoritmos modificados; This study presents the theoretical foundations of multivariable data modeling in state space by Subspace Methods for Systems Identification of linear time invariant, discrete time, systems. The work contains some basic concepts of dynamic systems, a little of history and the elements of systems identification, state space models and extended state space models. Two Subspace Methods are analyzed and applied...

Parameter estimation of state space models for univariate observations

Costa, Marco; Alpuim, Teresa
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
85.78%
This paper contributes to the problem of estimation of state space model parameters by proposing estimators for the mean, the autoregressive parameters and the noise variances which, contrarily to maximum likelihood, may be calculated without assuming any specific distribution for the errors. The estimators suggested widen the scope of the application of the generalized method of moments to some heteroscedastic models, as in the case of state-space models with varying coefficients, and give sufficient conditions for their consistency. The paper includes a simulation study comparing the proposed estimators with maximum likelihood estimators. Finally, these methods are applied to the calibration of the meteorological radar and estimation of area rainfall.

Adjustment of state space models in view of area rainfall estimation

Costa, Marco; Alpuim, Teresa
Fonte: John Wiley and Sons Publicador: John Wiley and Sons
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
85.75%
This paper uses state space models and the Kalman filter to merge weather radar and rain gauge measurements in order to improve area rainfall estimates. Particular attention is given to the estimation of state space model parameters because precipitation data clearly deviates from the normal distribution, and the commonly used maximum likelihood method is difficult to apply and does not perform well. This work is based on 17 storms occurring between September 1998 and November 2000 in an area including part of the Alenquer river hydrographical basin. Based on these data, the work aims to investigate the importance of the parameters estimation method to the accuracy of mean area precipitation estimates. It was possible to conclude that the distribution-free estimation methods produce, in general, better mean area rainfall estimates than the maximum likelihood. Copyright (C) 2010 John Wiley & Sons, Ltd.

Improvement of surface water quality variables modelling that incorporates a hydro-meteorological factor: a state-space approach

Gonçalves, A. Manuela; Costa, Marco
Fonte: Proceedings of the 26th International Workshop on Statistical Modelling Publicador: Proceedings of the 26th International Workshop on Statistical Modelling
Tipo: Conferência ou Objeto de Conferência
ENG
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75.8%
In this work it is constructed a hydro-meteorological factor to improve the adjustment of statistical time series models, such as state space models, of water quality variables by observing hydrological series (recorded in time and space) in a River basin. The hydro-meteorological factor is incorporated as a covariate in multivariate state space models fitted to homogeneous groups of monitoring sites. Additionally, in the modelling process it is considered a latent variable that allows incorporating a structural component, such as seasonality, in a dynamic way.

State Space Modelling to Increase the Accuracy of Weather Radar Estimation of Mean Area Precipitation

Costa, Marco; Alpuim, Teresa
Fonte: Editrice Democratica Sarda Publicador: Editrice Democratica Sarda
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
85.74%
This work compares some state space models for correcting the bias of the weather radar in Cruz do Leão, at Coruche, Portugal. Particular attention was given to the statistical adjustment of models, because precipitation data clearly deviated from the normal distribution. This work is based on 17 storms occurred between September of 1998 and November of 2000, in an area that includes part of the Alenquer river hydrographical basin.

A state-space and clustering approach for analysing the water quality in a river basin

Costa, Marco; Gonçalves, A. Manuela
Fonte: Editrice Democratica Sarda Publicador: Editrice Democratica Sarda
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
75.65%
The aim of this contribution is to apply the state-space models to identify homogeneous groups of water quality monitoring sites based on compar- ison of temporal dynamics of the concentration of pollutants in the surface water of a river basin. This comparison is performed using the Kullback information, adapting the approach used in Bengtsson and Cavanaugh (2007). The purpose of our study is to identify spatial and temporal patterns.

A note on prediction bias for state space models with estimated parameters

Monteiro, Magda; Costa, Marco
Fonte: American Institute of Physics Publicador: American Institute of Physics
Tipo: Conferência ou Objeto de Conferência
ENG
Relevância na Pesquisa
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This paper aims to discuss some problems on state space models with estimated parameters. While existing research focus on the prediction mean squared error, this work presents some results on bias propagation into forecast and filter predictions when the mean vector of the state is taking with an estimation bias, namely, non recursive analytical expression for them. In particular, it is discussed the impact of mean bias in invariant state space models.

THE U.S. and European M&A cycles: A Markov switching and state space approach

Cordovil, José Maria Pinto de Faria da Cunha
Fonte: NSBE - UNL Publicador: NSBE - UNL
Tipo: Dissertação de Mestrado
Publicado em /01/2013 ENG
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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics; Mergers and Acquisitions’ cycles have been, over the past decades, an extremely interesting field of research, raising numerous questions concerning its length, triggers or even its relationship with the economic cycle. In this Work Project I intent to contribute with new evidence, mainly for European merger waves, but also to support previous studies in what regards to merger waves. I have chosen nonlinear models, such as the Markov Switching and the State Space models, to characterize the merger data, due to the advantage of identifying structural changes. I have concluded that there is evidence of merger waves, both in the U.S. and in Europe, and the possible surge of a new merger wave in Europe.

Variabilidade de solos hidrom??rficos: uma abordagem de espa??o de estados; Variability of hydromorphic soils: a state space approach.

Aquino, Leandro Sanzi
Fonte: Universidade Federal de Pelotas; Agronomia; Programa de P??s-Gradua????o em Solos; UFPel; BR Publicador: Universidade Federal de Pelotas; Agronomia; Programa de P??s-Gradua????o em Solos; UFPel; BR
Tipo: Dissertação Formato: application/pdf
POR
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Soil land leveling is a technique used in low land areas and has the objective to improve agricultural use to facilitate the management of water both for irrigation and drainage operations, for the establishment of agricultural practices and crop harvest. However, it causes changes in the physical environment where the plant grows, and many studies have sought to identify the effect of this practice in the structure of soil spatial variability and in the relationship between the hydric-physical and chemical soil attributes. Thus, the objective of this study was to identify and characterize the structure of spatial variability of soil hydric-physical and chemical attributes of a low land soil, before and after land leveling, and to study the relationship between these soil attributes through an autoregressive state space model. In an experimental area of 0.81 ha belongs to Embrapa Clima Temperado situated in Cap??o do Le??o county, state of Rio Grande do Sul, Brazil, was established a regular grid of 100 points spaced 10 m apart in both directions. At each point, soil disturbed and undisturbed samples were collected at the depth of 0-0.20 m to determine, before and after land leveling, the following soil attributes: clay...

Business Cycle Analysis and VARMA Models

KASCHA, Christian; MERTENS, Karel
Fonte: European University Institute Publicador: European University Institute
Tipo: Trabalho em Andamento Formato: 451512 bytes; application/pdf; digital
EN
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75.77%
An important question in empirical macroeconomics is whether structural vector autoregressions (SVARs) can reliably discriminate between competing DSGE models. Several recent papers have sug- gested that one reason SVARs may fail to do so is because they are finite-order approximations to infinite-order processes. In this context, we investigate the performance of models that do not suffer from this type of misspecification. We estimate VARMA and state space models using simulated data from a standard economic model and compare true with estimated impulse responses. For our examples, we find that one cannot gain much by using algorithms based on a VARMA rep- resentation. However, algorithms that are based on the state space representation do outperform VARs. Unfortunately, these alternative estimates remain heavily biased and very imprecise. The findings of this paper suggest that the reason SVARs perform weakly in these types of simulation studies is not because they are simple finite-order approximations. Given the properties of the generated data, their fail- ure seems almost entirely due to the use of small samples.

A semiparametric state space model

Monteiro, André A.
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/octet-stream; application/octet-stream; application/pdf
Publicado em /09/2010 ENG
Relevância na Pesquisa
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This paper considers the problem of estimating a linear univariate Time Series State Space model for which the shape of the distribution of the observation noise is not specified a priori. Although somewhat challenging computationally, the simultaneous estimation of the parameters of the model and the unknown observation noise density is made feasible through a combination of Gaussian-sum Filtering and Smoothing algorithms and Kernel Density Estimation methods. The bottleneck in these calculations consists in avoiding the geometric increase, with time, of the number of simultaneous Kalman filter components. It is the aim of this paper to show that this can be achieved by the use of standard techniques from Cluster Analysis and unsupervised Classification. An empirical illustration of this new methodology is included; this consists in the application of a semiparametric version of the Local Level model to the analysis of the wellknown river Nile data series.

Estimation of State Space Models and Stochastic Volatility

Miller Lira, Shirley
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
EN
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Ma thèse est composée de trois chapitres reliés à l'estimation des modèles espace-état et volatilité stochastique. Dans le première article, nous développons une procédure de lissage de l'état, avec efficacité computationnelle, dans un modèle espace-état linéaire et gaussien. Nous montrons comment exploiter la structure particulière des modèles espace-état pour tirer les états latents efficacement. Nous analysons l'efficacité computationnelle des méthodes basées sur le filtre de Kalman, l'algorithme facteur de Cholesky et notre nouvelle méthode utilisant le compte d'opérations et d'expériences de calcul. Nous montrons que pour de nombreux cas importants, notre méthode est plus efficace. Les gains sont particulièrement grands pour les cas où la dimension des variables observées est grande ou dans les cas où il faut faire des tirages répétés des états pour les mêmes valeurs de paramètres. Comme application, on considère un modèle multivarié de Poisson avec le temps des intensités variables, lequel est utilisé pour analyser le compte de données des transactions sur les marchés financières. Dans le deuxième chapitre, nous proposons une nouvelle technique pour analyser des modèles multivariés à volatilité stochastique. La méthode proposée est basée sur le tirage efficace de la volatilité de son densité conditionnelle sachant les paramètres et les données. Notre méthodologie s'applique aux modèles avec plusieurs types de dépendance dans la coupe transversale. Nous pouvons modeler des matrices de corrélation conditionnelles variant dans le temps en incorporant des facteurs dans l'équation de rendements...

Essays on numerically efficient inference in nonlinear and non-Gaussian state space models, and commodity market analysis.

Djegnéné, Gbowan Barnabé
Fonte: Université de Montréal Publicador: Université de Montréal
Tipo: Thèse ou Mémoire numérique / Electronic Thesis or Dissertation
FR
Relevância na Pesquisa
85.82%
The first two articles build procedures to simulate vector of univariate states and estimate parameters in nonlinear and non Gaussian state space models. We propose state space speci fications that offer more flexibility in modeling dynamic relationship with latent variables. Our procedures are extension of the HESSIAN method of McCausland[2012]. Thus, they use approximation of the posterior density of the vector of states that allow to : simulate directly from the state vector posterior distribution, to simulate the states vector in one bloc and jointly with the vector of parameters, and to not allow data augmentation. These properties allow to build posterior simulators with very high relative numerical efficiency. Generic, they open a new path in nonlinear and non Gaussian state space analysis with limited contribution of the modeler. The third article is an essay in commodity market analysis. Private firms coexist with farmers' cooperatives in commodity markets in subsaharan african countries. The private firms have the biggest market share while some theoretical models predict they disappearance once confronted to farmers cooperatives. Elsewhere, some empirical studies and observations link cooperative incidence in a region with interpersonal trust...

Neural network and state-space models for studying relationships among soil properties

Timm,Luís Carlos; Gomes,Daniel Takata; Barbosa,Emanuel Pimentel; Reichardt,Klaus; Souza,Manoel Dornelas de; Dynia,José Flávio
Fonte: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz" Publicador: São Paulo - Escola Superior de Agricultura "Luiz de Queiroz"
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/08/2006 EN
Relevância na Pesquisa
75.8%
The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity. Studies of this kind are traditionally performed using static regression models, which do not take into account the involved spatial structure. This work has the objective of evaluating the relation between a time-consuming and "expensive" variable (like soil total nitrogen) and other simple, easier to measure variables (as for instance, soil organic carbon, pH, etc.). Two important classes of models (linear state-space and neural networks) are used for prediction and compared with standard uni- and multivariate regression models, used as reference. For an oat crop cultivated area, situated in Jaguariuna, SP, Brazil (22º41' S, 47º00' W) soil samples of a Typic Haplustox were collected from the plow layer at points spaced 2 m apart along a 194 m spatial transect. Recurrent neural networks and standard state-space models had a better predictive performance of soil total nitrogen as compared to the standard regression models. Among the standard regression models the Vector Auto-Regression model had a better predictive performance for soil total nitrogen.

Single source of error state space approach to the Beveridge Nelson decomposition

Anderson, Heather; Low, Chin Nam; Snyder, Ralph
Fonte: Elsevier Publicador: Elsevier
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
85.74%
We use single source of error state space models to perform Beveridge Nelson decompositions. These models exploit the perfect correlation between innovations in the permanent and transitory components, and their estimation incorporates direct estimation o

Unsupervised State-Space Modelling Using Reproducing Kernels

Tobar, Felipe; Djuri?, Petar M.; Mandic, Danilo P.
Fonte: IEEE Publicador: IEEE
Tipo: Article; accepted version
EN
Relevância na Pesquisa
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This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1109/TSP.2015.2448527.; A novel framework for the design of state-space models (SSMs) is proposed whereby the state-transition function of the model is parametrised using reproducing kernels. The nature of SSMs requires learning a latent function that resides in the state space and for which input-output sample pairs are not available, thus prohibiting the use of gradient-based supervised kernel learning. To this end, we then propose to learn the mixing weights of the kernel estimate by sampling from their posterior density using Monte Carlo methods. We first introduce an offline version of the proposed algorithm, followed by an online version which performs inference on both the parameters and the hidden state through particle filtering. The accuracy of the estimation of the state-transition function is first validated on synthetic data. Next, we show that the proposed algorithm outperforms kernel adaptive filters in the prediction of real-world time series, while also providing probabilistic estimates, a key advantage over standard methods.; Felipe Tobar acknowledges financial support from EPSRC grant number EP/L000776/1.

On Particle Methods for Parameter Estimation in General State-Space Models

Kantas, Nikolas; Doucet, Arnoud; Singh, Sumeetpal S.; Maciejowski, Jan; Chopin, Nicolas
Fonte: Department of Engineering, University of Cambridge Publicador: Department of Engineering, University of Cambridge
Tipo: Article; published version
EN
Relevância na Pesquisa
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This is the final version of the article. It first appeared from Institute of Mathematical Statistics via http://projecteuclid.org/euclid.ss/1439220716.; Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models.; N. Kantas was supported by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/J01365X/1 and programme grant on Control For Energy and Sustainability (EP/G066477/1). S.S. Singh?s research is partly funded by EPSRC under the First Grant Scheme (EP/G037590/1). A. Doucet?s research is partly funded by EPSRC (EP/K000276/1 and EP/K009850/1). N. Chopin?s research is partly by the ANR as part of the ?Investissements d?Avenir? program (ANR-11-LABEX-0047).

On Particle Methods for Parameter Estimation in State-Space Models

Kantas, Nikolas; Doucet, Arnaud; Singh, Sumeetpal S.; Maciejowski, Jan; Chopin, Nicolas
Fonte: Institute of Mathematical Statistics Publicador: Institute of Mathematical Statistics
Tipo: Article; published version
EN
Relevância na Pesquisa
95.85%
This is the final version of the article. It first appeared from the Institute of Mathematical Statistics via http://dx.doi.org/10.1214/14-STS511; Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal process-ing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models.; N. Kantas was supported by the Engineering and Physical Sciences Research Council (EPSRC) under grant EP/J01365X/1 and programme grant on Control For Energy and Sustainability (EP/G066477/1). S.S. Singh's research is partly funded by EPSRC under the First Grant Scheme (EP/G037590/1). A. Doucet's research is partly funded by EPSRC (EP/K000276/1 and EP/K009850/1). N. Chopin's research is partly by the ANR as part of the "Investissements d'Avenir" program (ANR-11-LABEX-0047).

Bayesian Analysis and Computational Methods for Dynamic Modeling

Niemi, Jarad
Fonte: Universidade Duke Publicador: Universidade Duke
Tipo: Dissertação Formato: 2993798 bytes; application/pdf
Publicado em //2009 EN_US
Relevância na Pesquisa
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Dynamic models, also termed state space models, comprise an extremely rich model class for time series analysis. This dissertation focuses on building state space models for a variety of contexts and computationally efficient methods for Bayesian inference for simultaneous estimation of latent states and unknown fixed parameters.

Chapter 1 introduces state space models and methods of inference in these models. Chapter 2 describes a novel method for jointly sampling the entire latent state vector in a nonlinear Gaussian state space model using a computationally efficient adaptive mixture modeling procedure. This method is embedded in an overall Markov chain Monte Carlo algorithm for estimating fixed parameters as well as states. In Chapter 3 the method of the previous chapter is implemented in a few illustrative

nonlinear models and compared to standard existing methods. This chapter also looks at the effect of the number of mixture components as well as length of the time series on the efficiency of the method. I then turn to an biological application in Chapter 4. I discuss modeling choices as well as derivation of the state space model to be used in this application. Parameter and state estimation are analyzed in these models for both simulated and real data. Chapter 5 extends the methodology introduced in Chapter 2 from nonlinear Gaussian models to general state space models. The method is then applied to a financial

stochastic volatility model on US $ - British £ exchange rates. Bayesian inference in the previous chapter is accomplished through Markov chain Monte Carlo which is suitable for batch analyses...

Redes neurais e modelos de espaço de estados para o estudo da relação entre propriedades do solo; Neural network and state-space models for studying relationships among soil properties

Timm, Luís Carlos; Gomes, Daniel Takata; Barbosa, Emanuel Pimentel; Reichardt, Klaus; Souza, Manoel Dornelas de; Dynia, José Flávio
Fonte: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz Publicador: Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
Tipo: info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion; ; ; ; ; Formato: application/pdf
Publicado em 01/08/2006 ENG
Relevância na Pesquisa
75.8%
The study of soil property relationships is of great importance in agronomy aiming for a rational management of environmental resources and an improvement of agricultural productivity. Studies of this kind are traditionally performed using static regression models, which do not take into account the involved spatial structure. This work has the objective of evaluating the relation between a time-consuming and "expensive" variable (like soil total nitrogen) and other simple, easier to measure variables (as for instance, soil organic carbon, pH, etc.). Two important classes of models (linear state-space and neural networks) are used for prediction and compared with standard uni- and multivariate regression models, used as reference. For an oat crop cultivated area, situated in Jaguariuna, SP, Brazil (22º41' S, 47º00' W) soil samples of a Typic Haplustox were collected from the plow layer at points spaced 2 m apart along a 194 m spatial transect. Recurrent neural networks and standard state-space models had a better predictive performance of soil total nitrogen as compared to the standard regression models. Among the standard regression models the Vector Auto-Regression model had a better predictive performance for soil total nitrogen.; O estudo da relação entre as propriedades do solo é de grande importância na área agronômica objetivando um manejo racional dos recursos naturais do meio ambiente e um aumento na produtividade agrícola. Tradicionalmente este estudo tem sido realizado usando modelos de regressão estática os quais não levam em consideração a estrutura espacial envolvida. Este trabalho teve o objetivo de avaliar a relação entre uma variável de determinação mais cara e demorada (por exemplo...