Relevant bibliographies by topics / Multivariate filter

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multivariate filter'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Multivariate filter"

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Vitek, Francis. "A Closed Form Multivariate Linear Filter." IMF Working Papers 18, no. 275 (2018): 1. http://dx.doi.org/10.5089/9781484388785.001.

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Chen, Qiuhui, Charles A. Micchelli, Silong Peng, and Yuesheng Xu. "Multivariate Filter Banks Having Matrix Factorizations." SIAM Journal on Matrix Analysis and Applications 25, no. 2 (January 2003): 517–31. http://dx.doi.org/10.1137/s0895479802412735.

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WHITCHER, BRANDON, and PETER F. CRAIGMILE. "MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS." International Journal of Wavelets, Multiresolution and Information Processing 02, no. 04 (December 2004): 567–87. http://dx.doi.org/10.1142/s0219691304000652.

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We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.
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García Infante, Juan Carlos, José de J. Medel Juárez, and Juan Carlos Sánchez García. "Neural fuzzy digital filtering: multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)." Ingeniería e Investigación 31, no. 1 (January 1, 2011): 184–92. http://dx.doi.org/10.15446/ing.investig.v31n1.20569.

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Multivariate identifier filters (multiple inputs and multiple outputs - MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back-propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error.
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International Monetary Fund. "Estimating Potential Output with a Multivariate Filter." IMF Working Papers 10, no. 285 (2010): 1. http://dx.doi.org/10.5089/9781455210923.001.

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Kang, Jieun, Heung-Kyu Ko, Ji Hoon Shin, Gi-Young Ko, Kyung-Wook Jo, Jin Won Huh, Yeon-Mok Oh, Sang-Do Lee, and Jae Seung Lee. "Practice patterns of retrievable inferior vena cava filters and predictors of filter retrieval in patients with pulmonary embolism." Vascular Medicine 22, no. 6 (September 7, 2017): 512–17. http://dx.doi.org/10.1177/1358863x17726596.

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Retrievable inferior vena cava (IVC) filters are increasingly used in patients with venous thromboembolism (VTE) who have contraindications to anticoagulant therapy. However, previous studies have shown that many retrievable filters are left permanently in patients. This study aimed to identify the common indications for IVC filter insertion, the filter retrieval rate, and the predictive factors for filter retrieval attempts. To this end, a retrospective cohort study was performed at a tertiary care center in South Korea between January 2010 and May 2016. Electronic medical charts were reviewed for patients with pulmonary embolism (PE) who underwent IVC filter insertion. A total of 439 cases were reviewed. The most common indication for filter insertion was a preoperative/procedural aim, followed by extensive iliofemoral deep vein thrombosis (DVT). Retrieval of the IVC filter was attempted in 44.9% of patients. The retrieval success rate was 93.9%. History of cerebral hemorrhage, malignancy, and admission to a nonsurgical department were the significant predictive factors of a lower retrieval attempt rate in multivariate analysis. With the increased use of IVC filters, more issues should be addressed before placing a filter and physicians should attempt to improve the filter retrieval rate.
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Jonsson, Lena, Elzbieta Plaza, and Bengt Hultman. "Experiences of nitrogen and phosphorus removal in deep-bed filters in the Stockholm area." Water Science and Technology 36, no. 1 (July 1, 1997): 183–90. http://dx.doi.org/10.2166/wst.1997.0042.

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Experimental studies of nutrient removal in a discontinuous downflow two-media filter on a pilot plant scale are described and compared with previously reported results from a continuous upflow filter (DynaSand) studied at full scale. Under controlled conditions both filters show low effluent values of total nitrogen and phosphorus. Removal mechanisms were more complex in the discontinuous downflow filter owing to the use of two media, deposition of sludge on the filter surface, and changes of the biological activity over time after backwashing. The discontinuous downflow filter was much influenced by the formation of nitrogen gas bubbles clogging the filter, while the influence of nitrogen gas formation in the DynaSand filter was minor. Multivariate methods were used to evaluate factors influencing nitrate and phosphate removal and the time passing before the filter became clogged.
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Blagrave, Patrick, Roberto Garcia-Saltos, Douglas Laxton, and Fan Zhang. "A Simple Multivariate Filter for Estimating Potential Output." IMF Working Papers 15, no. 79 (2015): 1. http://dx.doi.org/10.5089/9781475565133.001.

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ur Rehman, Naveed, and Danilo P. Mandic. "Filter Bank Property of Multivariate Empirical Mode Decomposition." IEEE Transactions on Signal Processing 59, no. 5 (May 2011): 2421–26. http://dx.doi.org/10.1109/tsp.2011.2106779.

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Chen, Qiuhui, Charles A. Micchelli, and Yuesheng Xu. "Biorthogonal multivariate filter banks from centrally symmetric matrices." Linear Algebra and its Applications 402 (June 2005): 111–25. http://dx.doi.org/10.1016/j.laa.2004.12.028.

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Dissertations / Theses on the topic "Multivariate filter"

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SALMAN, RAMIZ. "Identification of common economic cycles using optimal multivariate filters." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/394321.

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This thesis includes two essays that are focused on developing multivariate filter approaches to be used for extracting common cyclical components where the common components can be used as an estimator of a business cycle. The first chapter aims to develop an optimal multivariate filter in order to extract common cyclical components of macroeconomic indicators. The filter allows macroeconomic series to be modeled as a phase shifted version of a coinciding business cycle (BC) while keeping other time series components such as the stochastic trend and idiosyncratic shocks intact (i.e. they are individually specified for each series). Earlier studies of Rünstler (2004), Valle e Azevedo et al. (2006) have applied phase shift in the form of a delay parameter when specifying lead-lag cycles. However, the lead-lag relationship is defined by rotating the baseline cycle which leads to loss of information. This deficiency is especially important if one considers working in continuous time. Therefore, this paper improves on the former technique by allowing a more flexible phase shift mechanism on the original BC. This in turn should lead to more realistic estimates and filters considering that the underlying data is generated through a continuous time framework. The study starts by presenting a structure for bi-variate time series system and then extends to model to incorporate a structure for three time series and beyond. Kalman filter and smoothing recursions are applied to compute the smoothed cycle estimates and to construct the likelihood function. Using simulated data, we test both model specifications by carrying out a grid search of the initial delay parameter to see the likelihood behavior as the parameter moves into fractional neighborhoods. Afterwards, applying the methodology to a set of EU countries and macroeconomic indicators; the study aims to shed light to the presence of cyclical heterogeneity at country level economic activity for major EU member states. A second empirical study provides analysis on how the model can be implemented for assigning a lead/lag ordering to three main economic indicators of a single country. The second chapter implements a multivariate non-parametric filtering approach; the Vertical Multivariate Singular Spectrum Analysis (V-MSSA) of Hassani and Mahmoudvand (2013) and Golyandina et al. (2013). to be applied for identifying a common economic cycle indicator. The methodology is a data-driven procedure that can decompose a time series into many sub components. By exploiting this ability of the SSA, the paper aims to first extract cyclical components based on frequency characteristics and then follow by choosing only common cyclical component pairs with-in the business cycle frequency spectrum. These components will then be aggregated for constructing an EU region wide Business cycle indicator. The chapter outlines each steps of the algorithm that will eventually identify the SSA filter to act as a band-pass filter. The study then proceeds with simulation based data where the common cycle can be controlled and extracted a priori as a benchmark to the SSA-based filter estimates. The study follows with an empirical analysis similar to the framework set in Valle e Azevedo et al. (2006) with the aim to identify a Euro region business cycle indicator. The SSA based filter estimate is compared with Euro region economic activity indicators; the EuroCoin and the quarterly GDP growth rate of the EU area. Our results presents evidence of a successful alternative for tracing the cyclical position of the EU economy from a much smaller data set. Moreover, the constructed indicator also could serve as an unobserved proxy for a monthly growth cycle. A further analysis is also conducted to reveal whether the SSA based approach can be considered as an alternative to parametric filtering methods by providing results of common cycle extraction using Unobserved component model alternatives.
This thesis includes two essays that are focused on developing multivariate filter approaches to be used for extracting common cyclical components where the common components can be used as an estimator of a business cycle. The first chapter aims to develop an optimal multivariate filter in order to extract common cyclical components of macroeconomic indicators. The filter allows macroeconomic series to be modeled as a phase shifted version of a coinciding business cycle (BC) while keeping other time series components such as the stochastic trend and idiosyncratic shocks intact (i.e. they are individually specified for each series). Earlier studies of Rünstler (2004), Valle e Azevedo et al. (2006) have applied phase shift in the form of a delay parameter when specifying lead-lag cycles. However, the lead-lag relationship is defined by rotating the baseline cycle which leads to loss of information. This deficiency is especially important if one considers working in continuous time. Therefore, this paper improves on the former technique by allowing a more flexible phase shift mechanism on the original BC. This in turn should lead to more realistic estimates and filters considering that the underlying data is generated through a continuous time framework. The study starts by presenting a structure for bi-variate time series system and then extends to model to incorporate a structure for three time series and beyond. Kalman filter and smoothing recursions are applied to compute the smoothed cycle estimates and to construct the likelihood function. Using simulated data, we test both model specifications by carrying out a grid search of the initial delay parameter to see the likelihood behavior as the parameter moves into fractional neighborhoods. Afterwards, applying the methodology to a set of EU countries and macroeconomic indicators; the study aims to shed light to the presence of cyclical heterogeneity at country level economic activity for major EU member states. A second empirical study provides analysis on how the model can be implemented for assigning a lead/lag ordering to three main economic indicators of a single country. The second chapter implements a multivariate non-parametric filtering approach; the Vertical Multivariate Singular Spectrum Analysis (V-MSSA) of Hassani and Mahmoudvand (2013) and Golyandina et al. (2013). to be applied for identifying a common economic cycle indicator. The methodology is a data-driven procedure that can decompose a time series into many sub components. By exploiting this ability of the SSA, the paper aims to first extract cyclical components based on frequency characteristics and then follow by choosing only common cyclical component pairs with-in the business cycle frequency spectrum. These components will then be aggregated for constructing an EU region wide Business cycle indicator. The chapter outlines each steps of the algorithm that will eventually identify the SSA filter to act as a band-pass filter. The study then proceeds with simulation based data where the common cycle can be controlled and extracted a priori as a benchmark to the SSA-based filter estimates. The study follows with an empirical analysis similar to the framework set in Valle e Azevedo et al. (2006) with the aim to identify a Euro region business cycle indicator. The SSA based filter estimate is compared with Euro region economic activity indicators; the EuroCoin and the quarterly GDP growth rate of the EU area. Our results presents evidence of a successful alternative for tracing the cyclical position of the EU economy from a much smaller data set. Moreover, the constructed indicator also could serve as an unobserved proxy for a monthly growth cycle. A further analysis is also conducted to reveal whether the SSA based approach can be considered as an alternative to parametric filtering methods by providing results of common cycle extraction using Unobserved component model alternatives.
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Pereira, Ana Regina Nunes. "Multivariate Filtering with Common Factors." Master's thesis, Instituto Superior de Economia e Gestão, 2009. http://hdl.handle.net/10400.5/1148.

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Mestrado em Econometria Aplicada e Previsão
This study discusses four commonly used optimal approximations to the infinite order moving average filter that ideally extracts from a time series fluctuations within a specified range of periodicities. Based on our findings, we use two of those approximations in the estimation of two macroeconomic signals: business cycle fluctuations and medium to long run component of output growth rate. This study dis-tinguishes itself from related literature by showing how to successfully incorporate in the multivariate band-pass approximations factors estimated from a large panel of time series. As illustration, we apply these approximations to U.S. data. We evaluate the real-time performance of the indicators and provide forecasting comparisons. The results suggest that the multivariate indica¬tor outperforms the competing univariate indicator across all different settings considered. Moreover, multivariate methods that target smooth growth are useful to forecast quarterly GDP growth rate at short-term and to forecast yearly GDP growth.
Este estudo discute quatro aproximações óptimas ao filtro de medias moveis infinitas que idealmente isola de uma serie temporal flutuações compreendidas num determinado intervalo de periodicidades. De acordo com as nossas conclusões, utilizamos duas dessas aproximações na estimaçao de dois sinais macroeconómicos: flutuacoes de ciclo economico no produto e a componente de medio e longo prazo da taxa de crescimento do produto. Este estudo distingue-se da literatura corrente ao mostrar como integrar nas aproximacoes do filtro banda multivariado factores estimados a partir de um largo painel de sóeries temporais. Como ilustracao, aplicamos estas aproximacoes a dados dos E.U.A.. Avaliamos o desempenho dos in¬dicadores em tempo real e apresentamos comparacoes em termos de previsao. Os resultados sugerem que o indicador multivariado tem um desempenho claramente superior ao do indicador univariado em todos os cenóarios considerados. Adicionalmente, os móetodos multivariados que aproximam o crescimento alisado sao úteis na previsao da taxa de crescimento trimestral do PIB a curto prazo e para previsao do crescimento anual do PIB.
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LUCCHESE, Gianfranco. "Multivariate hedonic models for heterogeneous product prices in dynamic supply chains." Doctoral thesis, Università degli studi di Bergamo, 2012. http://hdl.handle.net/10446/26713.

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Identifying parameters for state-space models in high dimensioned cases requires a complex methodology. We offer an example of application for hedonic prices and the hyper-parameter estimation for dynamic supply chains. An algorithm is created based on the Kalman filter-smoother and Expectation-Maximization procerures. Stopping rules for the algorithm are analyzed and compared. We detected the best stopping rule for our environment. In this way, the hedonic prices estimated can be used for any decision process. The thesis point to an application in forecast analysis for product prices. Accurate forecasting of market price developments is essential in achieving superior market performance. Especially in oligopolistic markets for durable consumer products a robust understanding of selling prices is important, as it drives pricing behavior as well as procurement, inventory and production decisions. Moreover, a supply chain perspective is indispensable for pricing forecasts since companies not only compete for product sales but also for limited resources. The thesis explores the use of dynamic multivariate hedonics-based pricing models that explicitly model selling prices with the market valuation of constituting parts. The model is applied to TAC SCM, a supply-chain trading agent competition. To find unknown component prices series we apply the Kalman filter technique to smooth and forecast implicit prices using the EM algorithm. Finally, we present results of our analysis to establish the viability of this method.
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Castellanos, Lucia. "Statistical Models and Algorithms for Studying Hand and Finger Kinematics and their Neural Mechanisms." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/273.

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The primate hand, a biomechanical structure with over twenty kinematic degrees of freedom, has an elaborate anatomical architecture. Although the hand requires complex, coordinated neural control, it endows its owner with an astonishing range of dexterous finger movements. Despite a century of research, however, the neural mechanisms that enable finger and grasping movements in primates are largely unknown. In this thesis, we investigate statistical models of finger movement that can provide insights into the mechanics of the hand, and that can have applications in neural-motor prostheses, enabling people with limb loss to regain natural function of the hands. There are many challenges associated with (1) the understanding and modeling of the kinematics of fingers, and (2) the mapping of intracortical neural recordings into motor commands that can be used to control a Brain-Machine Interface. These challenges include: potential nonlinearities; confounded sources of variation in experimental datasets; and dealing with high degrees of kinematic freedom. In this work we analyze kinematic and neural datasets from repeated-trial experiments of hand motion, with the following contributions: We identified static, nonlinear, low-dimensional representations of grasping finger motion, with accompanying evidence that these nonlinear representations are better than linear representations at predicting the type of object being grasped over the course of a reach-to-grasp movement. In addition, we show evidence of better encoding of these nonlinear (versus linear) representations in the firing of some neurons collected from the primary motor cortex of rhesus monkeys. A functional alignment of grasping trajectories, based on total kinetic energy, as a strategy to account for temporal variation and to exploit a repeated-trial experiment structure. An interpretable model for extracting dynamic synergies of finger motion, based on Gaussian Processes, that decomposes and reduces the dimensionality of variance in the dataset. We derive efficient algorithms for parameter estimation, show accurate reconstruction of grasping trajectories, and illustrate the interpretation of the model parameters. Sound evidence of single-neuron decoding of interpretable grasping events, plus insights about the amount of grasping information extractable from just a single neuron. The Laplace Gaussian Filter (LGF), a deterministic approximation to the posterior mean that is more accurate than Monte Carlo approximations for the same computational cost, and that in an off-line decoding task is more accurate than the standard Population Vector Algorithm.
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Marhaba, Bassel. "Restauration d'images Satellitaires par des techniques de filtrage statistique non linéaire." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0502/document.

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Le traitement des images satellitaires est considéré comme l'un des domaines les plus intéressants dans les domaines de traitement d'images numériques. Les images satellitaires peuvent être dégradées pour plusieurs raisons, notamment les mouvements des satellites, les conditions météorologiques, la dispersion et d'autres facteurs. Plusieurs méthodes d'amélioration et de restauration des images satellitaires ont été étudiées et développées dans la littérature. Les travaux présentés dans cette thèse se concentrent sur la restauration des images satellitaires par des techniques de filtrage statistique non linéaire. Dans un premier temps, nous avons proposé une nouvelle méthode pour restaurer les images satellitaires en combinant les techniques de restauration aveugle et non aveugle. La raison de cette combinaison est d'exploiter les avantages de chaque technique utilisée. Dans un deuxième temps, de nouveaux algorithmes statistiques de restauration d'images basés sur les filtres non linéaires et l'estimation non paramétrique de densité multivariée ont été proposés. L'estimation non paramétrique de la densité à postériori est utilisée dans l'étape de ré-échantillonnage du filtre Bayésien bootstrap pour résoudre le problème de la perte de diversité dans le système de particules. Enfin, nous avons introduit une nouvelle méthode de la combinaison hybride pour la restauration des images basée sur la transformée en ondelettes discrète (TOD) et les algorithmes proposés à l'étape deux, et nos avons prouvé que les performances de la méthode combinée sont meilleures que les performances de l'approche TOD pour la réduction du bruit dans les images satellitaires dégradées
Satellite image processing is considered one of the more interesting areas in the fields of digital image processing. Satellite images are subject to be degraded due to several reasons, satellite movements, weather, scattering, and other factors. Several methods for satellite image enhancement and restoration have been studied and developed in the literature. The work presented in this thesis, is focused on satellite image restoration by nonlinear statistical filtering techniques. At the first step, we proposed a novel method to restore satellite images using a combination between blind and non-blind restoration techniques. The reason for this combination is to exploit the advantages of each technique used. In the second step, novel statistical image restoration algorithms based on nonlinear filters and the nonparametric multivariate density estimation have been proposed. The nonparametric multivariate density estimation of posterior density is used in the resampling step of the Bayesian bootstrap filter to resolve the problem of loss of diversity among the particles. Finally, we have introduced a new hybrid combination method for image restoration based on the discrete wavelet transform (DWT) and the proposed algorithms in step two, and, we have proved that the performance of the combined method is better than the performance of the DWT approach in the reduction of noise in degraded satellite images
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Lee, Anthony. "Towards smooth particle filters for likelihood estimation with multivariate latent variables." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1547.

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In parametrized continuous state-space models, one can obtain estimates of the likelihood of the data for fixed parameters via the Sequential Monte Carlo methodology. Unfortunately, even if the likelihood is continuous in the parameters, the estimates produced by practical particle filters are not, even when common random numbers are used for each filter. This is because the same resampling step which drastically reduces the variance of the estimates also introduces discontinuities in the particles that are selected across filters when the parameters change. When the state variables are univariate, a method exists that gives an estimator of the log-likelihood that is continuous in the parameters. We present a non-trivial generalization of this method using tree-based o(N²) (and as low as O(N log N)) resampling schemes that induce significant correlation amongst the selected particles across filters. In turn, this reduces the variance of the difference between the likelihood evaluated for different values of the parameters and the resulting estimator is considerably smoother than naively running the filters with common random numbers. Importantly, in practice our methods require only a change to the resample operation in the SMC framework without the addition of any extra parameters and can therefore be used for any application in which particle filters are already used. In addition, excepting the optional use of interpolation in the schemes, there are no regularity conditions for their use although certain conditions make them more advantageous. In this thesis, we first introduce the relevant aspects of the SMC methodology to the task of likelihood estimation in continuous state-space models and present an overview of work related to the task of smooth likelihood estimation. Following this, we introduce theoretically correct resampling schemes that cannot be implemented and the practical tree-based resampling schemes that were developed instead. After presenting the performance of our schemes in various applications, we show that two of the schemes are asymptotically consistent with the theoretically correct but unimplementable methods introduced earlier. Finally, we conclude the thesis with a discussion.
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Al, Chaer Toufic. "Robust control of multivariable systems : application to a three-phase shunt active filter in low voltage electrical networks." Poitiers, 2008. http://www.theses.fr/2008POIT2352.

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The work done in this thesis aims to combine the knowledge of two research areas: the automatic control area and the power electronics area, in order to elaborate a methodology for controlling a three-phase shunt active filter system. Indeed, this subject has been widely handled by the specialists in the field of power electronics in order to eliminate voltage and current harmonics in electrical distribution networks. Most of existing control strategies of active filters have been formulated as a current reference tracking control design problem. The alternative approach that we propose is to formulate a three-phase shunt active filter as a linear perturbation rejection problem with zero references. The linearity of the mathematical model enables us to use the H∞ control paradigm in order to design a multivariable linear robust H∞ controller. Such a controller is able to reject harmonics generated by the nonlinear loads, and to maintain the stability and performance of the system against the variations in the physical parameters of the electric circuit. In order to validate the proposed approach, simulations are carried out in Matlab/Simulink environment, and the control laws are implemented on an experimental test bench
Le travail effectué dans cette thèse a pour objectif de combiner les connaissances de deux domaines de recherche : l’automatique et l’ électronique de puissance afin de dégager une méthodologie pour contrôler un filtre actif parallèle triphasé. En effet, ce sujet est largement traité par les spécialistes de l’électronique de puissance pour éliminer les harmoniques de tension et de courant sur un réseau de distribution électrique. La plupart des stratégies de commande sont basées sur la formulation du problème du filtrage actif comme un problème de suivi de consignes classiquement utilisé dans ce domaine. L’ approche que nous proposons est de considérer le problème comme un problème linéaire de rejet de perturbations. La modélisation linéaire du système permet la synthèse d’une loi de commande robuste multivariable en vue d’ une stabilisation robuste et d’ une performance H∞ robuste. Cette loi de commande devra permettre d’ éliminer les harmoniques qui apparaissent sur le réseau, et de maintenir la stabilité et la performance du système vis-à-vis les incertitudes sur les paramètres du modèle. La validité de l’ approche proposée est vérifiée en simulation à partir de l’ outil logiciel Matlab/Simulink, puis par la mise en œuvre sur un banc expérimental
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Cunha, Camilla Lima. "Estudo da previsão de propriedades do biodiesel utilizando espectros de infravermelho e calibração multivariada." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=7293.

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O biodiesel tem sido amplamente utilizado como uma fonte de energia renovável, que contribui para a diminuição de demanda por diesel mineral. Portanto, existem várias propriedades que devem ser monitoradas, a fim de produzir e distribuir biodiesel com a qualidade exigida. Neste trabalho, as propriedades físicas do biodiesel, tais como massa específica, índice de refração e ponto de entupimento de filtro a frio foram medidas e associadas a espectrometria no infravermelho próximo (NIR) e espectrometria no infravermelho médio (Mid-IR) utilizando ferramentas quimiométricas. Os métodos de regressão por mínimos quadrados parciais (PLS), regressão de mínimos quadrados parciais por intervalos (iPLS), e regressão por máquinas de vetor de suporte (SVM) com seleção de variáveis por Algoritmo Genético (GA) foram utilizadas para modelar as propriedades mencionadas. As amostras de biodiesel foram sintetizadas a partir de diferentes fontes, tais como canola, girassol, milho e soja. Amostras adicionais de biodiesel foram adquiridas de um fornecedor da região sul do Brasil. Em primeiro lugar, o pré-processamento de correção de linha de base foi usado para normalizar os dados espectrais de NIR, seguidos de outros tipos de pré-processamentos que foram aplicados, tais como centralização dos dados na média, 1 derivada e variação de padrão normal. O melhor resultado para a previsão do ponto de entupimento de filtro a frio foi utilizando os espectros de Mid-IR e o método de regressão GA-SVM, com alto coeficiente de determinação da previsão, R2Pred=0,96 e baixo valor da Raiz Quadrada do Erro Médio Quadrático da previsão, RMSEP (C)= 0,6. Para o modelo de previsão da massa específica, o melhor resultado foi obtido utilizando os espectros de Mid-IR e regressão por PLS, com R2Pred=0,98 e RMSEP (g/cm3)= 0,0002. Quanto ao modelo de previsão para o índice de refração, o melhor resultado foi obtido utilizando os espectros de Mid-IR e regressão por PLS, com excelente R2Pred=0,98 e RMSEP= 0,0001. Para esses conjuntos de dados, o PLS e o SVM demonstraram sua robustez, apresentando-se como ferramentas úteis para a previsão das propriedades do biodiesel estudadas
Biodiesel has been widely used as a renewable energy source which contributes to the mineral diesel decrease demand. Therefore, there are several properties that must be monitored in order to produce and distribute biodiesel with the required quality. In this work, the biodiesel physical properties such as specific mass, refractive index and cold filter plugging point were measured and associated with near infrared spectroscopy (NIR) and mid-Infrared spectroscopy (mid-IR) spectra using chemometric tools. The Partial Least Squares Regression (PLS), Interval Partial Least Squares Regression (iPLS), and Support Vector Machines Regression (SVM) with variable selection by Genetic Algorithm (GA) methods were used to model the aforementioned properties. The biodiesel samples were synthesized from different sources such as canola, sunflower, corn, and soybean. Additional biodiesel samples were purchased from a Brazil South Region supplier. Firstly, the preprocessing baseline correction was used to normalize the NIR spectral data, following others preprocessing types were applied in such as the mean center, the first derivative and standard normal variate. The best result for predicting the cold filter plugging point was using Mid-IR spectra and GA-SVM regression method, with high coefficient determination of prediction, R2Pred = 0.94 and low value of the Root Mean Square Error of Prediction, RMSEP (C) = 0.7. For the specific mass prediction model, the best result was obtained using the Mid-IR spectrums and PLS regression, with the R2Pred = 0.98 and RMSEP (g/cm3) = 0.0002. As for a prediction model for the refractive index, the best result was obtained using the Mid-IR spectrums and PLS regression, with the R2Pred = 0.98 and RMSEP = 0.0001. For these datasets, the PLS and SVM models demonstrated theirs robustness, presenting themselves as useful tools for the biodiesel properties prediction studied
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Plappally, Anand Krishnan. "Theoretical and Empirical Modeling of Flow, Strength, Leaching and Micro-Structural Characteristics of V Shaped Porous Ceramic Water Filters." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1276860054.

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Frey, Roman. "Monte Carlo methods with application to the pricing of interest rate derivatives /." St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/03393436001/$FILE/03393436001.pdf.

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Books on the topic "Multivariate filter"

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Laxton, Douglas. A simple multivariate filter for the measurement of potential output. [Ottawa]: Bank of Canada, 1992.

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Laxton, Douglas. A simple multivariate filter for the measurement of potential output. [Ottawa]: Bank of Canada, 1992.

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Benes, Jaromir. A multivariate filter for measuring potential output and the NAIRU: Application to the Czech Republic. [Washington, D.C.]: International Monetary Fund, Asia and Pacific Dept., 2004.

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SAS Institute. JMP: Version 12 : multivariate methods. Cary, NC: SAS Institute, 2015.

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Thomas' calculus: Multivariable. Harlow: Addison-Wesley, 2009.

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N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. Carey, NC: SAS Institute, 1995.

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Khattree, Ravindra. Applied multivariate statistics with SAS software. 2nd ed. Cary, NC: SAS Institute, 1999.

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N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. 2nd ed. Cary, NC: SAS Institute, 1999.

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L, Hershberger Scott, ed. Multivariate statistical methods: A first course. Mahwah, N.J: Lawrence Erlbaum Associates, 1997.

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1941-, Stewart James, and Yasskin Philip B. 1949-, eds. Multivariable CalcLabs with Maple: For Stewart's fourth edition, Calculus, Multivariable calculus, Calculus--early transcendentals. Pacific Grove, CA: Brooks/Cole Pub., 1999.

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Book chapters on the topic "Multivariate filter"

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Stronegger, Willi-Julius. "Kalman Filter zur On-Line-Diskriminanz-Analyse von Verlaufskurven." In Multivariate Modelle, 123–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-95669-0_6.

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Jadid Abdulkadir, Said, and Suet-Peng Yong. "Unscented Kalman Filter for Noisy Multivariate Financial Time-Series Data." In Lecture Notes in Computer Science, 87–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44949-9_9.

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Blough, David K. "Intervention Analysis in Multivariate Time Series via the Kalman Filter." In Estimation and Analysis of Insect Populations, 389–403. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3664-1_28.

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Xu, Yonghong, Wenxue Hong, Na Chen, Xin Li, WenYuan Liu, and Tao Zhang. "Parallel Filter: A Visual Classifier Based on Parallel Coordinates and Multivariate Data Analysis." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 1172–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_121.

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Boonkla, Surasak, Masashi Unoki, and Stanislav S. Makhanov. "Robust Speech Analysis Based on Source-Filter Model Using Multivariate Empirical Mode Decomposition in Noisy Environments." In Speech and Computer, 580–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_70.

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Zeng, An, Dan Pan, Yang Haidong, and Xie Guangqiang. "Applications of Multivariate Time Series Analysis, Kalman Filter and Neural Networks in Estimating Capital Asset Pricing Model." In Modern Advances in Applied Intelligence, 507–16. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07467-2_53.

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Frühwirth-Schnatter, Sylvia. "Monitoring von ökologischen und biometrischen Prozessen mit statistischen Filtern." In Multivariate Modelle, 89–122. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-95669-0_5.

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Triantafyllopoulos, K. "Multivariate Stochastic Volatility Estimation Using Particle Filters." In Springer Proceedings in Mathematics & Statistics, 335–45. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0569-0_30.

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Occorsio, Donatella, and Woula Themistoclakis. "Uniform Weighted Approximation by Multivariate Filtered Polynomials." In Lecture Notes in Computer Science, 86–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39081-5_9.

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Li, Wenbin, Ning Zhong, and Chunnian Liu. "Combining Multiple Email Filters Based on Multivariate Statistical Analysis." In Lecture Notes in Computer Science, 729–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875604_81.

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Conference papers on the topic "Multivariate filter"

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Nam Anh, Dao. "Multivariate Filter for Saliency." In 2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2018. http://dx.doi.org/10.1109/mapr.2018.8337522.

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Abdul-Rahman, Shuzlina, Zeti-Azura Mohamed-Hussein, and Azuraliza Abu Bakar. "Multivariate filter and PSO in protein function classification." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5686158.

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Soyemi, Olusola O., Paul J. Gemperline, Lixia Zhang, DeLyle Eastwood, Hong Li, and Michael L. Myrick. "Novel filter design algorithm for multivariate optical computing." In Environmental and Industrial Sensing, edited by Tuan Vo-Dinh and Stephanus Buettgenbach. SPIE, 2001. http://dx.doi.org/10.1117/12.417462.

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Bollenbeck, Felix, Andreas Backhaus, and Udo Seiffert. "A multivariate wavelet-PCA denoising-filter for hyperspectral images." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080901.

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Lyu, Shuai, Haoran Mei, Limei Peng, Shih Yu Chang, and Jiang Mo. "Multivariate-aided Power-consumption Prediction Based on LSTM-Kalman Filter." In 2022 International Conference on Networking and Network Applications (NaNA). IEEE, 2022. http://dx.doi.org/10.1109/nana56854.2022.00100.

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Daojing Wang, Chao Zhang, and Xuemin Zhao. "Multivariate Laplace Filter: A heavy-tailed model for target tracking." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761002.

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Andersson, Ulrika, and Simon Godsill. "Optimum Kernel Particle Filter for Asymmetric Laplace Noise in Multivariate Models." In 2020 IEEE 23rd International Conference on Information Fusion (FUSION). IEEE, 2020. http://dx.doi.org/10.23919/fusion45008.2020.9190286.

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Lin, Yating, and Yiwen Zhong. "Software Defect Prediction Based on Data Sampling and Multivariate Filter Feature Selection." In 2018 2nd International Conference on Artificial Intelligence: Technologies and Applications (ICAITA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/icaita-18.2018.33.

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Liqing Di, Zhihua Xiong, Yujin Cao, and Xianhui Yang. "On-line Monitoring of Batch Processes Using Kalman Filter and Multivariate Statistical Methods." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714127.

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Lv, Baoxian, and Xinxian Tian. "The Properties of Multivariate Wavelet Packets Associated with Eight-Scaled Filter Bank Functions." In 2010 International Conference on e-Education, e-Business, e-Management, and e-Learning, (IC4E). IEEE, 2010. http://dx.doi.org/10.1109/ic4e.2010.87.

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Reports on the topic "Multivariate filter"

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De Castro-Valderrama, Marcela, Santiago Forero-Alvarado, Nicolás Moreno-Arias, and Sara Naranjo-Saldarriaga. Unraveling the Exogenous Forces Behind Analysts' Macroeconomic Forecasts. Banco de la República, December 2021. http://dx.doi.org/10.32468/be.1184.

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Modern macroeconomics focuses on the identification of the primitive exogenous forces generating business cycles. This is at odds with macroeconomic forecasts collected through surveys, which are about endogenous variables. To address this divorce, our paper uses a general equilibrium model as a multivariate filter to infer the shocks behind market analysts' forecasts and thus, unravel their implicit macroeconomic stories. By interpreting all analysts' forecasts through the same lenses, it is possible to understand the differences between projected endogenous variables as differences in the types and magnitudes of shocks. It also allows to explain market's uncertainty about the future in terms of analysts' disagreement about these shocks. The usefulness of the approach is illustrated by adapting the canonical SOE semi-structural model in Carabenciov et al. (2008a) to Colombia and then using it to filter forecasts of its Central Bank's Monthly Expectations Survey during the COVID-19 crisis.
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Clark, Todd E., Gergely Ganics, and Elmar Mertens. Constructing fan charts from the ragged edge of SPF forecasts. Federal Reserve Bank of Cleveland, November 2022. http://dx.doi.org/10.26509/frbc-wp-202236.

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We develop a model that permits the estimation of a term structure of both expectations and forecast uncertainty for application to professional forecasts such as the Survey of Professional Forecasters (SPF). Our approach exactly replicates a given data set of predictions from the SPF (or a similar forecast source) without measurement error. Our model captures fixed horizon and fixed-event forecasts, and can accommodate changes in the maximal forecast horizon available from the SPF. The model casts a decomposition of multi-period forecast errors into a sequence of forecast updates that may be partially unobserved, resulting in a multivariate unobserved components model. In our empirical analysis, we provide quarterly term structures of expectations and uncertainty bands. Our preferred specification features stochastic volatility in forecast updates, which improves forecast performance and yields model estimates of forecast uncertainty that vary over time. We conclude by constructing SPF-based fan charts for calendar-year forecasts like those published by the Federal Reserve. Replication files are available at https://github.com/elmarmertens/ClarkGanicsMertensSPFfancharts.
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