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Добірка наукової літератури з теми "Fixed-Time and robust estimation"

Автор: Grafiati

Опубліковано: 12 квітня 2025

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Статті в журналах з теми "Fixed-Time and robust estimation"

OLIINYK, VIACHESLAV, and VOLODYMYR LUKIN. "USE OF SIMILARITY METRICS IN ROBUST TIME DELAY ESTIMATION." Herald of Khmelnytskyi National University. Technical sciences 319, no. 2 (April 27, 2023): 224–30. http://dx.doi.org/10.31891/2307-5732-2023-319-1-224-230.

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This paper addresses the task of time delay and direction of arrival estimation for a source of the wideband signal using two sensors with fixed displacement. The peculiarity of the task statement is that a limited time of signal observation is supposed and additive noise is assumed non-Gaussian with a heavy-tail distribution. This leads to a high probability of abnormal estimates for the conventional signal processing method based on cross-correlation. To decrease this probability, it is proposed to reformulate the task of cross-correlation processing to the task of similarity estimation between two data arrays. This allows using different similarity metrics, particularly those that have less sensitivity to outliers in data (impulse noise), and, thus, provide better robustness for non-Gaussian environments typical for several applications of time delay estimation. More than ten different similarity metrics are considered for the model of the symmetric α-stable distribution describing noise properties. It is shown that some metrics including cosine distance, Hellinger, and some others are able to provide sufficiently better accuracy of time delay estimation both in the sense of less RMSE of normal estimates and probability of abnormal estimates for typical values of α and a wide range of γ values for symmetric α-stable distribution.

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Thombs, Ryan P. "A Guide to Analyzing Large N, Large T Panel Data." Socius: Sociological Research for a Dynamic World 8 (January 2022): 237802312211176. http://dx.doi.org/10.1177/23780231221117645.

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Fixed effects estimation of a static model with robust or panel corrected standard errors is commonly used to model large N, large T panel data. However, this approach is biased and inconsistent in the presence of dynamic misspecification, slope heterogeneity, and cross-sectional dependence. Common correlated effects estimation of a dynamic model has been advanced to address these issues but is rarely used in sociology. Here, I provide an overview of the large N, large T panel data literature, and I conduct an array of Monte Carlo experiments to compare the fixed effects estimator to the common correlated effects estimator regarding the aforementioned issues. I show that fixed effects estimation with robust or panel corrected standard errors do not address these problems, which is most evident with high levels of slope heterogeneity and lag misspecification, and its performance worsens as the time dimension expands. In contrast, the common correlated effects estimator produces superior estimates as T increases and is robust to slope heterogeneity and cross-sectional dependence. Following the experiments, I present an example by examining the drivers of fossil fuel consumption at the U.S. state level from 1960 to 2018, and I conclude by presenting a decision-making framework for researchers to use to make informed decisions when modeling large N, large T panel data.

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Wu, Tao, Zhengjiang Liu, and Guoyou Shi. "Practical Fixed-Time Robust Containment Control of Multi-ASVs with Collision Avoidance." Journal of Marine Science and Engineering 12, no. 12 (December 23, 2024): 2363. https://doi.org/10.3390/jmse12122363.

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A practical fixed-time robust containment control method for multiple autonomous surface vehicles (multi-ASVs) is proposed in this study. This method addresses the containment control problem of multi-ASVs, considering both collision risks and external disturbances. This control scheme improves the cooperative performance of the formation and guarantees safe collision avoidance behavior. First, to enable the online estimation of unknown time-varying disturbances from the external environment, a fixed-time disturbance observer (FNDO) is designed based on fixed-time control theory. Second, the distributed kinematic controller is modified to include the partial derivatives of the artificial potential energy function (APEF), thereby preventing collisions among multi-ASVs. Third, by applying fixed-time theory, graph theory, and fixed-time dynamic surface control techniques, a practical fixed-time robust containment controller for multi-ASVs is proposed. Additionally, the entire closed-loop control system is guaranteed to be practical and fixed-time stable through stability analysis. Finally, the proposed control strategy has been validated by simulation results.

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Youssef, Ahmed Hassen, Mohamed Reda Abonazel, and Elsayed G. Ahmed. "Robust M Estimation for Poisson Panel Data Model with Fixed Effects: Method, Algorithm, Simulation, and Application." Statistics, Optimization & Information Computing 12, no. 5 (June 3, 2024): 1292–305. http://dx.doi.org/10.19139/soic-2310-5070-1996.

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The fixed effects Poisson (FEP) model is one of the most important for the count data when the data containperiods and cross-sectional units. The maximum likelihood (ML) estimation method for the FEP model provides good results in the absence of outliers, but it is affected by outliers. So, we introduce in this paper robust estimators for the FEP model. These estimators yield stable and good results in case of the presence of outliers. The Monte Carlo simulation study and empirical application were conducted to assess the performance of the non-robust fixed Poisson maximum likelihood (FPML) estimator and the robust estimators: fixed Poisson Huber (FPHR), fixed Poisson Hampel (FPHM) and fixed Poisson Tukey (FPTK). The results of simulation and application show that robust estimators are better than FPML estimator when the count panel data contains outliers. In addition, FPTK is more efficient than other robust estimators.

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Magnussen, Steen. "Robust fixed-count density estimation with virtual plots." Canadian Journal of Forest Research 44, no. 4 (April 2014): 377–82. http://dx.doi.org/10.1139/cjfr-2013-0288.

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Fixed-count sampling (plotless) remains attractive for forest inventories in difficult terrains and for their control of the number (k) of trees to measure. Although recent fixed-count estimators of density (PDE) are less biased than older ones, the risk of a nontrivial bias remains a deterrent. A recently published PDE based on a generic algorithm for predicting distances to the k + m nearest tree (m = 1, 2, ...) has attractive properties in terms of average bias and average root mean squared errors across a wide spectrum of spatial point patterns. However, the risk of a sizeable bias remains an issue. Sensitivity to spatial patterns is seen as its main weakness. It is hypothesized that a new PDE with robust properties will mitigate the bias issue and encourage wider use. To this end, a new PDE estimator is proposed. It builds on a mixture of observed and predicted distances to a set of k + m nearest trees to generate counts of actual and virtual trees inside a circle with a data-driven fixed radius. The proposed new robust fixed-count density estimator achieved an average absolute bias of 1.2% when tested across a wide range of point patterns (54 actual and four simulated). The maximum absolute bias was 4.4%, a significant reduction when compared with otherwise attractive alternative PDEs. Root mean squared errors and coverage of 95% confidence intervals were also encouraging. The deterrent bias issue in PDEs has been sharply reduced with the proposed estimator.

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Chai, Dashuai, Yipeng Ning, Shengli Wang, Wengang Sang, Jianping Xing, and Jingxue Bi. "A Robust Algorithm for Multi-GNSS Precise Positioning and Performance Analysis in Urban Environments." Remote Sensing 14, no. 20 (October 15, 2022): 5155. http://dx.doi.org/10.3390/rs14205155.

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In this paper, we propose a partial ambiguity method of global navigation satellite system (GNSS) reliable positioning based on a robust estimation model to address the problems of the low reliability and availability of GNSS positioning in urban complex environments. First, the high-precision observations selected on the basis of the signal-to-noise ratio (SNR) were used to solve ambiguities. Then, the fixed ambiguities were used as constraints to solve the ambiguities of low-quality observations. The robust estimation method was used to reduce the impact of outliers for the ambiguity solutions. The robust estimation was also used to solve the position parameters to reduce the influence of the residual errors and uncorrected ambiguities for GNSS high-accuracy positioning. Static and dynamic data were used to evaluate the proposed algorithm. These experiments show that the proposed algorithm with the robust estimation can reduce the fixed time of ambiguity initialization, compared with the conventional algorithm without the robust estimation. The positioning accuracy and solution rate are similar regardless of whether the robust estimation is used in the GNSS unblocked environment. In blocked environments, the solution rate improves to more than 99%, and the three-dimensional (3D) position accuracy improves by more than 70% when the robust estimation is used. When the observation number of simulated small gross error accounts for 40.91% of total observations, the centimeter-level positioning accuracy can still be obtained via several robust estimation models. In the urban blocked environment, the IGG (Institute of Geodesy and Geophysics) III scheme has a better performance than other robust schemes discussed in this paper with regard to the positioning performance and computational efficiency.

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Xi, Axing, and Yuanli Cai. "A Nonlinear Finite-Time Robust Differential Game Guidance Law." Sensors 22, no. 17 (September 2, 2022): 6650. http://dx.doi.org/10.3390/s22176650.

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In this paper, a robust differential game guidance law is proposed for the nonlinear zero-sum system with unknown dynamics and external disturbances. First, the continuous-time nonlinear zero-sum differential game problem is transformed into solving the nonlinear Hamilton–Jacobi–Isaacs equation, a time-varying cost function is developed to reflect the fixed terminal time, and the robust guidance law is developed to compensate for the external disturbance. Then, a novel neural network identifier is designed to approximate the unknown nonlinear dynamics with online weight tuning. Subsequently, an online critic neural network approximator is presented to estimate the cost function, and time-varying activation functions are considered to deal with the fixed final time problem. An adaptive weight tuning law is given, where two additional terms are added to ensure the stability of the closed-loop nonlinear system and so as to meet the terminal cost at a fixed final time. Furthermore, the uniform ultimate boundedness of the closed-loop system and the critic neural network weights estimation error are proven based upon the Lyapunov approach. Finally, some simulation results are presented to demonstrate the effectiveness of the proposed robust differential game guidance law for nonlinear interception.

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Nakamori, Seiichi. "Robust Recursive Least-Squares Fixed-Point Smoother and Filter using Covariance Information in Linear Continuous-Time Stochastic Systems with Uncertainties." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 20 (May 13, 2024): 56–66. http://dx.doi.org/10.37394/232014.2024.20.2.

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This study develops robust recursive least-squares (RLS) fixed-point smoothing and filtering algorithms for signals in linear continuous-time stochastic systems with uncertainties. The algorithms use covariance information, such as the cross-covariance function of the signal with the observed value and the autocovariance function of the degraded signal. A finite Fourier cosine series expansion approximates these functions. Additive white Gaussian noise is present in the observation of the degraded signal. A numerical simulation compares the estimation accuracy of the proposed robust RLS filter with the robust RLS Wiener filter, showing similar mean square values (MSVs) of the filtering errors. The MSVs of the proposed robust RLS fixed-point smoother are also compared to those of the proposed robust RLS filter.

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Mentz, Raúl P., and Carlos I. Martínez. "Robust estimation in time series." Test 11, no. 2 (December 2002): 385–404. http://dx.doi.org/10.1007/bf02595713.

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Guerrier, Stephane, Roberto Molinari, and Maria-Pia Victoria-Feser. "Estimation of Time Series Models via Robust Wavelet Variance." Austrian Journal of Statistics 43, no. 4 (June 13, 2014): 267–77. http://dx.doi.org/10.17713/ajs.v43i4.45.

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A robust approach to the estimation of time series models is proposed. Taking froma new estimation method called the Generalized Method of Wavelet Moments (GMWM)which is an indirect method based on the Wavelet Variance (WV), we replace the classicalestimator of the WV with a recently proposed robust M-estimator to obtain a robustversion of the GMWM. The simulation results show that the proposed approach can beconsidered as a valid robust approach to the estimation of time series and state-spacemodels.

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Більше джерел

Дисертації з теми "Fixed-Time and robust estimation"

Zhang, Yuqing. "Fixed-time algebraic distributed state estimation for linear systems." Electronic Thesis or Diss., Bourges, INSA Centre Val de Loire, 2025. http://www.theses.fr/2025ISAB0001.

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Au cours des dernières décennies, le déploiement massif de capteurs embarqués en réseau dotés des capacités de communication dans des systèmes à grande échelle a suscité un intérêt croissant de la part des chercheurs dans le domaine de l’estimation distribuée. Cette thèse vise à développer une méthode d’estimation d’état distribuée algébrique à temps fixe pour les systèmes linéaires à temps variant d’ordre entier et les systèmes linéaires à temps invariant d’ordre fractionnaire dans des environnements bruités, en concevant un ensemble d’estimateurs locaux d’ordre réduit au niveau des capteurs en réseau.Pour ce faire, nous introduisons d’abord un schéma d’estimation distribuée en définissant un ensemble denoeuds récupérés à chaque noeud de capteur, basé sur un graphe dirigé plus relâché que celui qui est fortement connecté. En utilisant cet ensemble récupéré, nous construisons une transformation inversible pour la décomposition d’observabilité afin d’identifier le sous-système local observable de chaque noeud. De plus, cette transformation permet une représentation distribuée de l’état entier du système à chaque noeud sous forme de combinaison linéaire de son propre état local observable et de ceux des noeuds de son ensemble récupéré. Cela garantit que chaque noeud peut atteindre l’estimation distribuée d’état, à condition que les estimations des états locaux observables soient assurées. En conséquence, ce schéma distribué se concentre sur l’estimation des états locaux observables, permettant une estimation distribuée à travers le réseau de capteurs.En nous appuyant sur cette base, afin de traiter l’estimation algébrique à temps fixe pour chaque sous-système local observable identifié, différentes méthodes d’estimation à fonctions modulatrices sont explorées pour établir des formules algébriques indépendantes des conditions initiales, les rendant efficaces en tant qu’estimateurs locaux de ordre réduit à temps fixe. Pour les systèmes linéaires à temps variant d’ordre entier, la transformation utilisée pour développer le schéma d’estimation distribuée aboutit à une forme normale linéaire partiellement observable à temps variant. La méthode des fonctions modulatrices généralisées est ensuite appliquée pour estimer chaque état local observable à travers des formules intégrales algébriques des sorties du système et de leurs dérivées. Pour les systèmes linéaires à temps invariant d’ordre fractionnaire, une autre transformation est utilisée pour convertir chaque sous-système local observable identifié sous une forme normale observable d’ordre fractionnaire, permettant l’application de la méthode d’estimation à fonctions modulatrices généralisées d’ordre fractionnaire. Cette méthode calcule directement des formules intégrales algébriques pour les variables pseudo-état locales observables.Ensuite, en combinant ces formules algébriques avec la représentation distribuée dérivée, nous réalisons l’estimation d’état distribuée algébrique à temps fixe pour les systèmes étudiés. De plus, une analyse d’erreur est réalisée pour démontrer la robustesse de l’estimateur distribué conçu en présence de bruits continus de processus et de mesure, ainsi que de bruits discrets de mesure. Enfin, plusieurs exemples de simulation sont fournis pour valider l’efficacité du schéma d’estimation distribuée proposé
In recent decades, the widespread deployment of networked embedded sensors with communication capabilities in large-scale systems has drawn significant attentions fromresearchers to the field of distributed estimation. This thesis aims to develop a fixed-time algebraic distributed state estimation method for both integer-order linear time-varying systems and fractional-order linear-invariant systems in noisy environments, by designing a set of reduced-order local estimators at the networked sensors.To achieve this, we first introduce a distributed estimation scheme by defining a recovered node set at each sensor node, based on a digraph assumption that is more relaxed than the strongly connected one. Using this recovered set, we construct an invertible transformation for the observability decomposition to identify each node’s local observable subsystem. Additionally, this transformation allows for a distributed representation of the entire system state at each node by a linear combination of its own local observable state and those of the nodes in its recovered set. This ensures that each node can achieve the distributed state estimation, provided that the estimations for the set of local observable states are ensured. As a result, this distributed scheme focuses on estimating the local observable states, enabling distributed estimation across the sensor network.Building on this foundation, to address the fixed-time algebraic state estimation for each identified local observable subsystem, different modulating functions estimation methods are investigated to derive the initial-condition-independent algebraic formulas, making them effective as reduced-order local fixed-time estimators. For integer-order linear time-varying systems, the transformation used in developing distributed estimation scheme yields a linear time-varying partial observable normal form. The generalized modulating functions method is then applied to estimate each local observable state through algebraic integral formulas of system outputs and their derivatives. For fractional-order linear-invariant systems, another transformation is used to convert each identified local observable subsystem into a fractional-order observable normal form, allowing for the application of the fractional-order generalized modulating functions estimation method. This method directly computes algebraic integral formulas for local observable pseudo-state variables.Subsequently, by combining these algebraic formulas with the derived distributed representation, we achieve the fixed-time algebraic distributed state estimation for the studied systems. Additionally, an error analysis is conducted to demonstrate the robustness of the designed distributed estimator in the presence of both continuous process and measurement noises, as well as discrete measurement noises. Finally, several simulation examples are provided to validate the effectiveness of the proposed distributed estimation scheme

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Copeland, Andrew David 1978. "Robust motion estimation in the presence of fixed pattern noise." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87395.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 41-42).
by Andrew David Copeland.
M.Eng.

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Kwan, Tan Hwee. "Robust estimation for structural time series models." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/2809/.

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This thesis aims at developing robust methods of estimation in order to draw valid inference from contaminated time series. We concentrate on additive and innovation outliers in structural time series models using a state space representation. The parameters of interest are the state, hyperparameters and coefficients of explanatory variables. Three main contributions evolve from the research. Firstly, a filter named the approximate Gaussian sum filter is proposed to cope with noisy disturbances in both the transition and measurement equations. Secondly, the Kalman filter is robustified by carrying over the M-estimation of scale for i.i.d observations to time-dependent data. Thirdly, robust regression techniques are implemented to modify the generalised least squares transformation procedure to deal with explanatory variables in time series models. All the above procedures are tested against standard non-robust estimation methods for time series by means of simulations. Two real examples are also included.

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Sinha, Sanjoy Kumar. "Some aspects of robust estimation in time series analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ57354.pdf.

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Zheng, Xueying, and 郑雪莹. "Robust joint mean-covariance model selection and time-varying correlation structure estimation for dependent data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899703.

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In longitudinal and spatio-temporal data analysis, repeated measurements from a subject can be either regional- or temporal-dependent. The correct specification of the within-subject covariance matrix cultivates an efficient estimation for mean regression coefficients. In this thesis, robust estimation for the mean and covariance jointly for the regression model of longitudinal data within the framework of generalized estimating equations (GEE) is developed. The proposed approach integrates the robust method and joint mean-covariance regression modeling. Robust generalized estimating equations using bounded scores and leverage-based weights are employed for the mean and covariance to achieve robustness against outliers. The resulting estimators are shown to be consistent and asymptotically normally distributed. Robust variable selection method in a joint mean and covariance model is considered, by proposing a set of penalized robust generalized estimating equations to estimate simultaneously the mean regression coefficients, the generalized autoregressive coefficients and innovation variances introduced by the modified Cholesky decomposition. The set of estimating equations select important covariate variables in both mean and covariance models together with the estimating procedure. Under some regularity conditions, the oracle property of the proposed robust variable selection method is developed. For these two robust joint mean and covariance models, simulation studies and a hormone data set analysis are carried out to assess and illustrate the small sample performance, which show that the proposed methods perform favorably by combining the robustifying and penalized estimating techniques together in the joint mean and covariance model. Capturing dynamic change of time-varying correlation structure is both interesting and scientifically important in spatio-temporal data analysis. The time-varying empirical estimator of the spatial correlation matrix is approximated by groups of selected basis matrices which represent substructures of the correlation matrix. After projecting the correlation structure matrix onto the space spanned by basis matrices, varying-coefficient model selection and estimation for signals associated with relevant basis matrices are incorporated. The unique feature of the proposed model and estimation is that time-dependent local region signals can be detected by the proposed penalized objective function. In theory, model selection consistency on detecting local signals is provided. The proposed method is illustrated through simulation studies and a functional magnetic resonance imaging (fMRI) data set from an attention deficit hyperactivity disorder (ADHD) study.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy

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Kovac, Arne. "Wavelet thresholding for unequally time-spaced data." Thesis, University of Bristol, 1999. http://hdl.handle.net/1983/2088715a-7792-4032-bb76-83e3b0389b94.

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Skoglund, Johan. "Robust Real-Time Estimation of Region Displacements in Video Sequences." Licentiate thesis, Linköping : Department of Electrical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8006.

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LaMaire, Richard O. "Robust time and frequency domain estimation methods in adaptive control." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14795.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.
MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Supported, in part, by the NASA Ames & Langley Research Centers, the Office of Naval Research, and the National Science Foundation.
Bibliography: v. 2, leaves 334-337.
by Richard Orville LaMaire.
Ph.D.

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Staerman, Guillaume. "Functional anomaly detection and robust estimation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT021.

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L’engouement pour l’apprentissage automatique s’étend à presque tous les domaines comme l’énergie, la médecine ou la finance. L’omniprésence des capteurs met à disposition de plus en plus de données avec une granularité toujours plus fine. Une abondance de nouvelles applications telles que la surveillance d’infrastructures complexes comme les avions ou les réseaux d’énergie, ainsi que la disponibilité d’échantillons de données massives, potentiellement corrompues, ont mis la pression sur la communauté scientifique pour développer de nouvelles méthodes et algorithmes d’apprentissage automatique fiables. Le travail présenté dans cette thèse s’inscrit dans cette ligne de recherche et se concentre autour de deux axes : la détection non-supervisée d’anomalies fonctionnelles et l’apprentissage robuste, tant du point de vue pratique que théorique.La première partie de cette thèse est consacrée au développement d’algorithmes efficaces de détection d’anomalies dans le cadre fonctionnel. Plus précisément, nous introduisons Functional Isolation Forest (FIF), un algorithme basé sur le partitionnement aléatoire de l’espace fonctionnel de manière flexible afin d’isoler progressivement les fonctions les unes des autres. Nous proposons également une nouvelle notion de profondeur fonctionnelle basée sur l’aire de l’enveloppe convexe des courbes échantillonnées, capturant de manière naturelle les écarts graduels de centralité. Les problèmes d’estimation et de calcul sont abordés et diverses expériences numériques fournissent des preuves empiriques de la pertinence des approches proposées. Enfin, afin de fournir des recommandations pratiques, la performance des récentes techniques de détection d’anomalies fonctionnelles est évaluée sur deux ensembles de données réelles liés à la surveillance des hélicoptères en vol et à la spectrométrie des matériaux de construction.La deuxième partie est consacrée à la conception et à l’analyse de plusieurs approches statistiques, potentiellement robustes, mêlant la profondeur de données et les estimateurs robustes de la moyenne. La distance de Wasserstein est une métrique populaire résultant d’un coût de transport entre deux distributions de probabilité et permettant de mesurer la similitude de ces dernières. Bien que cette dernière ait montré des résultats prometteurs dans de nombreuses applications d’apprentissage automatique, elle souffre d’une grande sensibilité aux valeurs aberrantes. Nous étudions donc comment tirer partie des estimateurs de la médiane des moyennes (MoM) pour renforcer l’estimation de la distance de Wasserstein avec des garanties théoriques. Par la suite, nous introduisons une nouvelle fonction de profondeur statistique dénommée Affine-Invariante Integrated Rank-Weighted (AI-IRW). Au-delà de l’analyse théorique effectuée, des résultats numériques sont présentés, confirmant la pertinence de cette profondeur. Les sur-ensembles de niveau des profondeurs statistiques donnent lieu à une extension possible des fonctions quantiles aux espaces multivariés. Nous proposons une nouvelle mesure de similarité entre deux distributions de probabilité. Elle repose sur la moyenne de la distance de Hausdorff entre les régions quantiles, induites par les profondeur de données, de chaque distribution. Nous montrons qu’elle hérite des propriétés intéressantes des profondeurs de données telles que la robustesse ou l’interprétabilité. Tous les algorithmes développés dans cette thèse sont accessible en ligne
Enthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) together with the availability of massive data samples has put pressure on the scientific community to develop new reliable Machine-Learning methods and algorithms. The work presented in this thesis focuses around two axes: unsupervised functional anomaly detection and robust learning, both from practical and theoretical perspectives.The first part of this dissertation is dedicated to the development of efficient functional anomaly detection approaches. More precisely, we introduce Functional Isolation Forest (FIF), an algorithm based on randomly splitting the functional space in a flexible manner in order to progressively isolate specific function types. Also, we propose the novel notion of functional depth based on the area of the convex hull of sampled curves, capturing gradual departures from centrality, even beyond the envelope of the data, in a natural fashion. Estimation and computational issues are addressed and various numerical experiments provide empirical evidence of the relevance of the approaches proposed. In order to provide recommendation guidance for practitioners, the performance of recent functional anomaly detection techniques is evaluated using two real-world data sets related to the monitoring of helicopters in flight and to the spectrometry of construction materials.The second part describes the design and analysis of several robust statistical approaches relying on robust mean estimation and statistical data depth. The Wasserstein distance is a popular metric between probability distributions based on optimal transport. Although the latter has shown promising results in many Machine Learning applications, it suffers from a high sensitivity to outliers. To that end, we investigate how to leverage Medians-of-Means (MoM) estimators to robustify the estimation of Wasserstein distance with provable guarantees. Thereafter, a new statistical depth function, the Affine-Invariant Integrated Rank-Weighted (AI-IRW) depth is introduced. Beyond the theoretical analysis carried out, numerical results are presented, providing strong empirical confirmation of the relevance of the depth function proposed. The upper-level sets of statistical depths—the depth-trimmed regions—give rise to a definition of multivariate quantiles. We propose a new discrepancy measure between probability distributions that relies on the average of the Hausdorff distance between the depth-based quantile regions w.r.t. each distribution and demonstrate that it benefits from attractive properties of data depths such as robustness or interpretability. All algorithms developed in this thesis are open-sourced and available online

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Chapman, Michael Addison. "Adaptation and Installation of a Robust State Estimation Package in the Eef Utility." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/31432.

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Robust estimation methods have been successfully applied to the problem of power system state estimation in a real-time environment. The Schweppe-type GM-estimator with the Huber psi-function (SHGM) has been fully installed in conjunction with a topology processor in the EEF utility, headquartered in Fribourg, Switzerland. Some basic concepts of maximum likelihood estimation and robust analysis are reviewed, and applied to the development of the SHGM-estimator. The algorithms used by the topology processor and state estimator are presented, and the superior performance of the SHGM-estimator over the classic weighted least squares estimator is demonstrated on the EEF network. The measurement configuration of the EEF network has been evaluated, and suggestions for its reinforcement have been proposed.
Master of Science

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Книги з теми "Fixed-Time and robust estimation"

Ladlow, Peter Thomas. Robust parameter estimation techniques for time-varying processes. Birmingham: University of Birmingham, 1998.

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Subrahmanyam, Allamaraju, and Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.

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Robust time and frequency domain estimation methods in adaptive control. Cambridge, Mass: Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 1987.

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Subrahmanyam, Allamaraju, and Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.

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Subrahmanyam, Allamaraju, and Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.

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Subrahmanyam, Allamaraju, and Ganti Prasada Rao. Identification of Continuous-Time Systems: Linear and Robust Parameter Estimation. Taylor & Francis Group, 2019.

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Anjum, Rani Lill, and Stephen Mumford. Same Cause, Same Effect. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198733669.003.0005.

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Hume gave the classic formulation of the regularity theory. The causal laws are fixed by the pattern of regularity in events. We have a conceptual constraint on the notion of causal law: the effects that laws describe must be repeatable and robust, applying at every time and place. From the same cause, therefore, we can infer the same effect. And by modus tollens, from a different effect, we infer a different cause. Apparent counterexamples can be marginalized, treated as exceptions, outliers, non-respondents, or effects of background noise or interference. This suggests that scientists accept the broadly Humean notion of causation as regularity.

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Hankin, David, Michael S. Mohr, and Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.

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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.

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Частини книг з теми "Fixed-Time and robust estimation"

Subrahmanyam, Allamaraju, and Ganti Prasada Rao. "Robust Parameter Estimation." In Identification of Continuous-Time Systems, 63–79. First edition. | New York, N.Y. : CRC Press/Taylor & Francis Group, 2020. | Series: Engineering systems and sustainability: CRC Press, 2019. http://dx.doi.org/10.1201/9780429352850-4.

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Mangoubi, Rami S. "Discrete-Time Robust Estimation." In Robust Estimation and Failure Detection, 43–84. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1586-1_3.

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Agostinelli, C. "Robust Time Series Estimation via Weighted Likelihood." In Developments in Robust Statistics, 1–16. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-642-57338-5_1.

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Seong, Junyeong, Sungjun Park, and Kunsoo Huh. "Robust Lane Keeping Control with Estimation of Cornering Stiffness and Model Uncertainty." In Lecture Notes in Mechanical Engineering, 272–78. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_39.

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AbstractThis paper introduces an adaptive lane-keeping control strategy that adapts to varying cornering stiffness while ensuring robustness against uncertainties. The system consists of three blocks: an Interacting Multiple Model (IMM) cornering stiffness estimator, a cornering stiffness uncertainty estimator, and a Robust Model Predictive Controller (RMPC). Improvements in estimation accuracy are achieved through a novel IMM probability derivation method, and the uncertainty estimator utilizes the IMM probability matrix to obtain reliable uncertainty boundaries. Real-time cornering stiffness estimations are integrated into the RMPC for adaptive model predictions. Uncertainty boundaries provide robustness against estimation error in the RMPC by constraint tightening and smoothing techniques. The performance of the estimator and controller is validated in simulations, where the overall control performance is compared to that of the Model Predictive Control (MPC) based on static cornering stiffness.

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Petersen, Ian R., and Andrey V. Savkin. "Discrete-Time Set-Valued State Estimation." In Robust Kalman Filtering for Signals and Systems with Large Uncertainties, 71–87. Boston, MA: Birkhäuser Boston, 1999. http://dx.doi.org/10.1007/978-1-4612-1594-3_5.

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Wu, Renbiao, Qiongqiong Jia, Lei Yang, and Qing Feng. "Application of RELAX in Time Delay Estimation." In Principles and Applications of RELAX: A Robust and Universal Estimator, 101–57. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6932-2_4.

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da Silva, Nuno Pinho, and João Paulo Costeira. "Robust Global Mosaic Topology Estimation for Real-Time Applications." In Lecture Notes in Computer Science, 1250–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559573_151.

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Gershon, Eli, and Uri Shaked. "Robust Estimation of Linear-Switched Systems with Dwell Time." In Advances in H∞ Control Theory, 61–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16008-1_5.

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Cheng, Yu, Ilias Diakonikolas, and Rong Ge. "High-Dimensional Robust Mean Estimation in Nearly-Linear Time." In Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms, 2755–71. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2019. http://dx.doi.org/10.1137/1.9781611975482.171.

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Reich, Sebastian. "Frequentist Perspective on Robust Parameter Estimation Using the Ensemble Kalman Filter." In Mathematics of Planet Earth, 237–58. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18988-3_15.

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AbstractStandard maximum likelihood or Bayesian approaches to parameter estimation for stochastic differential equations are not robust to perturbations in the continuous-in-time data. In this paper, we give a rather elementary explanation of this observation in the context of continuous-time parameter estimation using an ensemble Kalman filter. We employ the frequentist perspective to shed new light on two robust estimation techniques; namely subsampling the data and rough path corrections. We illustrate our findings through a simple numerical experiment.

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Тези доповідей конференцій з теми "Fixed-Time and robust estimation"

Koizumi, Kakeru, and Hiroshi Watanabe. "Event-based Robust 3D Pose Estimation Using Time Series Data." In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC), 1223–27. IEEE, 2024. http://dx.doi.org/10.1109/aic61668.2024.10731089.

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Lv, Yuezhang, Yunzhou Zhang, Xiaoyu Zhao, Wu Li, Jian Ning, and Yang Jin. "CTA-LO: Accurate and Robust LiDAR Odometry Using Continuous-Time Adaptive Estimation." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 12034–40. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611453.

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Pnevmatikakis, Aristodemos, and Lazaros Polymenakos. "Robust Estimation of Background for Fixed Cameras." In 2006 15th International Conference on Computing. IEEE, 2006. http://dx.doi.org/10.1109/cic.2006.63.

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Lee, You-Seok, and Hyoung-Nam Kim. "Noise-Robust Channel Estimation for DVB-T Fixed Receptions." In 2007 Digest of Technical Papers International Conference on Consumer Electronics. IEEE, 2007. http://dx.doi.org/10.1109/icce.2007.341562.

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Roy, Shibdas, Ian R. Petersen, and Elanor H. Huntington. "Adaptive continuous homodyne phase estimation using robust fixed-interval smoothing." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580312.

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Verling, Sebastian L., and Roland Siegwart. "Robust Wind Estimation for Fixed Wing UAVs in Surveying Applications." In AIAA Scitech 2019 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2019. http://dx.doi.org/10.2514/6.2019-0373.

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McPhee, Hamish, Jean-Yves Tourneret, David Valat, Philippe Paimblanc, Jérome Delporte, and Yoan Grégoire. "A Robust Time Scale Based on Maximum Likelihood Estimation." In 54th Annual Precise Time and Time Interval Systems and Applications Meeting. Institute of Navigation, 2023. http://dx.doi.org/10.33012/2023.18701.

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Abu Bakar, Nor Mazlina, and Habshah Midi. "The Applications of Robust Estimation in Fixed Effect Panel Data Model." In Proceedings of the 1st Aceh Global Conference (AGC 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/agc-18.2019.54.

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Wenqiang Liu and Zili Deng. "Robust steady-state Kalman filter for uncertain discrete-time system." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280188.

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Zhang, Zhaoyu, and Haibin Duan. "Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot^*." In 2023 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2023. http://dx.doi.org/10.1109/robio58561.2023.10354695.

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Звіти організацій з теми "Fixed-Time and robust estimation"

Galindo, Arturo, and Marcela Meléndez Arjona. Corporate Tax Stimulus and Investment in Colombia. Inter-American Development Bank, April 2010. http://dx.doi.org/10.18235/0010933.

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This paper uses a yearly dataset of plant-level investment in Colombian firms during the period 1997 to 2007 to assess the impact of a tax incentive for firms that invest in fixed assets implemented in 2004. A positive and statistically significant correlation is found between the boom observed in investment and the adoption of the tax policy. However, the correlation vanishes when year-specific effects are controlled for. This result is robust to changes in the empirical specification, changes in estimation techniques, the inclusion of additional controls, and changes in the data set, among other tests. Overall, it is concluded that the tax stimulus analyzed was ineffective in promoting investment in Colombia.

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Rojas-Bernal, Alejandro, and Mauricio Villamizar-Villegas. Pricing the exotic: Path-dependent American options with stochastic barriers. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/be.1156.

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We develop a novel pricing strategy that approximates the value of an American option with exotic features through a portfolio of European options with different maturities. Among our findings, we show that: (i) our model is numerically robust in pricing plain vanilla American options; (ii) the model matches observed bids and premiums of multidimensional options that integrate Ratchet, Asian, and Barrier characteristics; and (iii) our closed-form approximation allows for an analytical solution of the option’s greeks, which characterize the sensitivity to various risk factors. Finally, we highlight that our estimation requires less than 1% of the computational time compared to other standard methods, such as Monte Carlo simulations.

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Schling, Maja, Nicolás Pazos, Leonardo Corral, and Marisol Inurritegui. The Effects of Tenure Security on Women's Empowerment and Food Security: Evidence From a Land Regularization Program in Ecuador. Inter-American Development Bank, December 2023. http://dx.doi.org/10.18235/0005355.

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This paper evaluates the impact of a rural land administration program in Ecuador on female empowerment and household food security. Using a double robust estimation that combines the difference-in-difference approach with inverse probability weighting, we explore whether receiving a georeferenced cadastral map of ones parcel provides women with increased bargaining power, empowering them to participate more actively in productive and consumption decision-making that leads to improved diversification of the production portfolio and the households food security. Although we find no significant effects on aggregate levels of empowerment, results show that female beneficiaries became more empowered with regards to access to resources, particularly in terms of applying for and receiving credit. Program participation also significantly affected womens time use, as beneficiary women spent more hours working in non-agricultural activities, investing in their own businesses, and generating off-farm wages. Households who received jointly titled cadastral maps also increased their food security and shifted their production portfolios towards crops and livestock products of both higher market and nutritional value. These results suggest that increasing informal tenure security through cadastral mapping may spur female empowerment, which enables women to increase their bargaining power within the household in order to improve their own and the family's overall welfare.

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi та Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, січень 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.

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Pinkovskiy, Maxim, Xavier Sala-i-Martin, Kasey Chatterji-Len, and William Nober. Inequality Within Countries is Falling: Underreporting Robust Estimates of World Poverty, Inequality, and the Global Distribution of Income. Federal Reserve Bank of New York, September 2024. http://dx.doi.org/10.59576/sr.1125.

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Household surveys suffer from persistent and growing underreporting. We propose a novel procedure to adjust reported survey incomes for underreporting by estimating a model of misreporting whose main parameter of interest is the elasticity of regional national accounts income to regional survey income, which is closely related to the elasticity of underreporting with respect to income. We find this elasticity to be substantial but roughly constant over time, implying a large but relatively constant correction to survey-derived inequality estimates. Underreporting of income by the bottom 50 percent of the world income distribution has become particularly important in recent decades. We reconfirm the findings of the literature that global poverty and inequality have declined dramatically between 1980 and 2019. Finally, we find that within-country inequality is falling on average and has been largely constant since the 1990s.

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Stucchi, Rodolfo, Alessandro Maffioli, Sofía Rojo, and Victoria Castillo. Knowledge Spillovers of Innovation Policy through Labor Mobility: An Impact Evaluation of the FONTAR Program in Argentina. Inter-American Development Bank, February 2014. http://dx.doi.org/10.18235/0011534.

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Although knowledge spillovers are at the core of the innovation policy's justification, they have never been properly measured by any impact evaluation. This paper fills this gap by estimating the spillover effects of the FONTAR program in Argentina. We use an employer-employee matched panel dataset with the entire population of firms and workers in Argentina for the period 2002-2010. This dataset allows us to track the mobility of qualified workers from FONTAR beneficiary firms to other firms and, therefore, to identify firms that indirectly benefit from the program through knowledge diffusion. We use a combination of fixed effect and matching to estimate the causal effect-direct and indirect-of the program on various measures of performance. Our findings are robust to a placebo test based on anticipatory effects and show that the program increased employment, wages, and the exporting probability of both direct and indirect beneficiaries. The analysis of the dynamic of these effects confirms that performance does not improve immediately after the treatment for neither direct nor indirect beneficiaries.

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Arizala, Francisco, Eduardo A. Cavallo, and Arturo Galindo. Financial Development and TFP Growth: Cross-Country and Industry-Level Evidence. Inter-American Development Bank, June 2009. http://dx.doi.org/10.18235/0010917.

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This paper estimates the impact of financial development on industry-level total factor productivity (TFP) growth using a largely unexploited panel of 77 countries with data for 26 manufacturing industries for the years 1963 to 2003. A significant relationship is found between financial development and industry-level TFP growth when controlling for country-time and industry-time fixed effects. The results are both statistically and economically significant. TFP growth can accelerate up to 0.6 percent per year, depending on the external finance requirement of industries, following a one standard deviation increase in financial development. The results are robust to different samples and specifications.

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Chong, Alberto E., and Eliana La Ferrara. Television and Divorce: Evidence from Brazilian Novelas. Inter-American Development Bank, January 2009. http://dx.doi.org/10.18235/0010906.

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This paper studies the link between television and divorce in Brazil by exploiting variation in the timing of availability of the signal of Rede Globo -the network that had a virtual monopoly on telenovelas in the countryacross municipal areas. Using three rounds of Census data (1970, 1980 and 1991) and controlling for area fixed effects and for time-varying characteristics, the paper finds that the share of women who are separated or divorced increases significantly after the Globo signal becomes available. The effect is robust to controlling for potential determinants of Globos entry strategy and is stronger for relatively smaller areas, where the signal reaches a higher fraction of the population.

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González, Francisco, José E. Gutiérrez, and José María Serena. Shadow seniority? Lending relationships and borrowers’ selective default. Madrid: Banco de España, June 2024. http://dx.doi.org/10.53479/36695.

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This paper analyzes how lending relationships affect firms’ incentives to default, drawing on loan-level data in Spain. We provide new evidence showing that firms first default on loans from less important (“non-main”) banks to preserve their most valuable lending relationships. Our findings also indicate that banks integrate this borrower behavior into their credit risk management because the most important banks within a borrower’s set of lending relationships recognize lower discretionary loan impairments. The results are robust to alternative difference-in-difference (DID) analyses and control for potential bank forbearance, loan characteristics, and a variety of time-varying bank and firm fixed effects.

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Torero, Máximo, and Jaime Saavedra-Chanduví. Union Density Changes and Union Effects on Firm Performance in Peru. Inter-American Development Bank, September 2002. http://dx.doi.org/10.18235/0011249.

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The paper documents the sharp reduction in union density in Peru between 1986 and 1998, in a context of large macroeconomic fluctuations, structural reforms and changes in the Collective Bargaining Law in 1993. The authors find that a blue-collar job, a permanent contract, higher education and working in a large firm increase the likelihood of unionization, but only before the legislative change. Using a panel of firms for the manufacturing sector for the period 1994-1996, a negative impact of unions on profits for all firm sizes is found. In the econometric analysis, a significant negative effect even after controlling for firm and sector characteristics and firm fixed effects is found. There is some evidence that this effect diminishes over time, consistent with the reduction in union density during that period, but the reduction is not robust to different specifications.

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