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Academic literature on the topic 'Optimal estimator'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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Journal articles on the topic "Optimal estimator"

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Lui, Kung-Jong. "Notes on Use of the Composite Estimator: an Improvement of the Ratio Estimator." Journal of Official Statistics 36, no. 1 (2020): 137–49. http://dx.doi.org/10.2478/jos-2020-0007.

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AbstractThis article discusses use of the composite estimator with the optimal weight to reduce the variance (or the mean-squared-error, MSE) of the ratio estimator. To study the practical usefulness of the proposed composite estimator, a Monte Carlo simulation is performed comparing the bias and MSE of composite estimators (with estimated optimal weight and with known optimal weight) with those of the simple expansion and the ratio estimators. Two examples, one regarding the estimation of dead fir trees via an aerial photo and the other regarding the estimation of the average sugarcane acres per county, are included to illustrate the use of the composite estimator developed here.

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Cordue, Patrick L. "Designing optimal estimators for fish stock assessment." Canadian Journal of Fisheries and Aquatic Sciences 55, no. 2 (1998): 376–86. http://dx.doi.org/10.1139/f97-228.

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Many estimation procedures are used in the provision of fisheries stock assessment advice. Most procedures use estimators that have optimal large-sample characteristics, but these are often applied to small-sample data sets. In this paper, a minimum integrated average expected loss (MIAEL) estimation procedure is presented. By its design a MIAEL estimator has optimal characteristics for the type of data it is applied to, given that the model assumptions of the particular problem are satisfied. The estimation procedure is developed within a decision-theoretic framework and illustrated with a Bernoulli and a fisheries example. MIAEL estimation is related to optimal Bayes estimation, as both procedures seek an estimator that minimizes an integrated loss function. In most fisheries applications a global MIAEL estimator will be difficult to determine, and a MIAEL estimator will need to be found within a given class of estimators. "Squared f-error," a generalization of the common squared error loss function is defined. It is shown that an estimator can be improved (for a given squared f-error loss function) by using its best linear transformation which is the MIAEL estimator within the class of linear transformations (in f space).

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Setiawan, Ezra Putranda, and Dedi Rosadi. "APPLICATION OF ROBUST REGRESSION FOR PORTFOLIO OPTIMIZATION." Matrix Science Mathematic 7, no. 1 (2023): 07–15. http://dx.doi.org/10.26480/msmk.01.2023.07.15.

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The single-index model is a portfolio optimization method that uses each asset’s beta’. In general, the beta is estimated using the return data by the least square method. However, the return data frequently contains several outliers, so the estimation resulting from the least square method is inaccurate. This study examines several beta estimators from robust regression methods, namely the least absolute value estimator, M-estimator, LMS-estimator, LTS-estimator, MM-estimator, and Tau estimator to estimate the beta of each asset and make an optimal portfolio based on this estimated value. We also evaluate the effect of robust beta estimators on the stability and performance of each portfolio. We present the Sharpe ratio and some turnover measures, namely the l-period portfolio turnover, maximum turnover, lower bound single-asset turnover, and lower bound multiple-asset turnover. Among various estimators used here, the Tau estimator is the best estimator to replace the OLS for estimating the beta.

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Martínez, Sergio, María del Mar Rueda, and María Dolores Illescas. "The optimization problem of quantile and poverty measures estimation based on calibration." Journal of Computational and Applied Mathematics 405 (June 12, 2020): 113054. https://doi.org/10.5281/zenodo.10583622.

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New calibrated estimators of quantiles and poverty measures are proposed. These estimators combine the incorporation of auxiliary information provided by auxiliary variables related to the variable of interest by calibration techniques with the selection of optimal calibration points under simple random sampling without replacement. The problem of selecting calibration points that minimize the asymptotic variance of the quantile estimator is addressed. Once the problem is solved, the definition of the new quantile estimator requires that the optimal estimator of the distribution function on which it is based verifies the properties of the distribution function. Through a theorem, the nondecreasing monotony property for the optimal estimator of the distribution function is established and the corresponding optimal estimator can be defined. This optimal quantile estimator is also used to define new estimators for poverty measures. Simulation studies with real data from the Spanish living conditions survey compares the performance of the new estimators against various methods proposed previously, where some resampling techniques are used for the variance estimation. Based on the results of the simulation study, the proposed estimators show a good performance and are a reasonable alternative to other estimators.

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Zhao, Quanshui. "ASYMPTOTICALLY EFFICIENT MEDIAN REGRESSION IN THE PRESENCE OF HETEROSKEDASTICITY OF UNKNOWN FORM." Econometric Theory 17, no. 4 (2001): 765–84. http://dx.doi.org/10.1017/s0266466601174050.

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We consider a linear model with heteroskedasticity of unknown form. Using Stone's (1977, Annals of Statistics 5, 595–645) k nearest neighbors (k-NN) estimation approach, the optimal weightings for efficient least absolute deviation regression are estimated consistently using residuals from preliminary estimation. The reweighted least absolute deviation or median regression estimator with the estimated weights is shown to be equivalent to the estimator using the true but unknown weights under mild conditions. Asymptotic normality of the estimators is also established. In the finite sample case, the proposed estimators are found to outperform the generalized least squares method of Robinson (1987, Econometrica 55, 875–891) and the one-step estimator of Newey and Powell (1990, Econometric Theory 6, 295–317) based on a Monte Carlo simulation experiment.

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Zakerzadeh, Hojatollah, Ali Akbar Jafari, and Mahdieh Karimi. "Optimal Shrinkage Estimations for the Parameters of Exponential Distribution Based on Record Values." Revista Colombiana de Estadística 39, no. 1 (2016): 33–44. http://dx.doi.org/10.15446/rce.v39n1.55137.

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<p>This paper studies shrinkage estimation after the preliminary test for the parameters of exponential distribution based on record values. The optimal value of shrinkage coefficients is also obtained based on the minimax regret criterion. The maximum likelihood, pre-test, and shrinkage estimators are compared using a simulation study. The results to estimate the scale parameter show that the optimal shrinkage estimator is better than the maximum likelihood estimator in all cases, and when the prior guess is near the true value, the pre-test estimator is better than shrinkage estimator. The results to estimate the location parameter show that the optimal shrinkage estimator is better than maximum likelihood estimator when a prior guess is close<br />to the true value. All estimators are illustrated by a numerical example.</p>

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Lakhdar, Yissam, and El Hassan Sbai. "Online Variable Kernel Estimator." International Journal of Operations Research and Information Systems 8, no. 1 (2017): 58–92. http://dx.doi.org/10.4018/ijoris.2017010104.

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In this work, the authors propose a novel method called online variable kernel estimation of the probability density function (pdf). This new online estimator combines the characteristics and properties of two estimators namely nearest neighbors estimator and the Parzen-Rosenblatt estimator. Their approach allows a compact online adaptation of the estimated probability density function from the new arrival data. The performance of the online variable kernel estimator (OVKE) depends on the choice of the bandwidth. The authors present in this article a new technique for determining the optimal smoothing parameter of OVKE based on the maximum entropy principle (MEP). The robustness and performance of the proposed approach are demonstrated by examples of online estimation of real and simulated data distributions.

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Hirukawa, Masayuki. "A TWO-STAGE PLUG-IN BANDWIDTH SELECTION AND ITS IMPLEMENTATION FOR COVARIANCE ESTIMATION." Econometric Theory 26, no. 3 (2009): 710–43. http://dx.doi.org/10.1017/s0266466609990089.

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The two most popular bandwidth choice rules for kernel HAC estimation have been proposed by Andrews (1991) and Newey and West (1994). This paper suggests an alternative approach that estimates an unknown quantity in the optimal bandwidth for the HAC estimator (called normalized curvature) using a general class of kernels, and derives the optimal bandwidth that minimizes the asymptotic mean squared error of the estimator of normalized curvature. It is shown that the optimal bandwidth for the kernel-smoothed normalized curvature estimator should diverge at a slower rate than that of the HAC estimator using the same kernel. An implementation method of the optimal bandwidth for the HAC estimator, which is analogous to the one for probability density estimation by Sheather and Jones (1991), is also developed. The finite sample performance of the new bandwidth choice rule is assessed through Monte Carlo simulations.

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Pearce, Mark E., Earl T. Campbell, and Pieter Kok. "Optimal quantum metrology of distant black bodies." Quantum 1 (July 26, 2017): 21. http://dx.doi.org/10.22331/q-2017-07-26-21.

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Measurements of an object's temperature are important in many disciplines, from astronomy to engineering, as are estimates of an object's spatial configuration. We present the quantum optimal estimator for the temperature of a distant body based on the black body radiation received in the far-field. We also show how to perform separable quantum optimal estimates of the spatial configuration of a distant object, i.e. imaging. In doing so we necessarily deal with multi-parameter quantum estimation of incompatible observables, a problem that is poorly understood. We compare our optimal observables to the two mode analogue of lensed imaging and find that the latter is far from optimal, even when compared to measurements which are separable. To prove the optimality of the estimators we show that they minimise the cost function weighted by the quantum Fisher information---this is equivalent to maximising the average fidelity between the actual state and the estimated one.

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Gonzalez, Luz Mery, Julio M. Singer, and Edward J. Stanek III. "Finite Population Mixed Models for Pretest-Posttest Designs with Response Errors." Revista Colombiana de Estadística 45, no. 1 (2022): 125–48. http://dx.doi.org/10.15446/rce.v45n1.93196.

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We consider a finite population mixed model that accommodates response errors and show how to obtain optimal estimators of the finite population parameters in a pretest-posttest context. We illustrate the method with the estimation of the difference in gain between two interventions and consider a simulation study to compare the empirical version of the proposed estimator (obtained by replacing variance components with estimates) with the estimator obtained via covariance analysis usually employed in such settings. The results indicate that in many instances, the proposed estimator has a smaller mean squared error than that obtained from the standard analysis of covariance model.

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Dissertations / Theses on the topic "Optimal estimator"

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Laurich, P. H. (Peter Hermann) Carleton University Dissertation Engineering Electrical. "Modeling of a wave generator and the design of an optimal estimator." Ottawa, 1988.

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Sun, Xusheng. "Optimal distributed detection and estimation in static and mobile wireless sensor networks." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44825.

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This dissertation develops optimal algorithms for distributed detection and estimation in static and mobile sensor networks. In distributed detection or estimation scenarios in clustered wireless sensor networks, sensor motes observe their local environment, make decisions or quantize these observations into local estimates of finite length, and send/relay them to a Cluster-Head (CH). For event detection tasks that are subject to both measurement errors and communication errors, we develop an algorithm that combines a Maximum a Posteriori (MAP) approach for local and global decisions with low-complexity channel codes and processing algorithms. For event estimation tasks that are subject to measurement errors, quantization errors and communication errors, we develop an algorithm that uses dithered quantization and channel compensation to ensure that each mote's local estimate received by the CH is unbiased and then lets the CH fuse these estimates into a global one using a Best Linear Unbiased Estimator (BLUE). We then determine both the minimum energy required for the network to produce an estimate with a prescribed error variance and show how this energy must be allocated amongst the motes in the network. In mobile wireless sensor networks, the mobility model governing each node will affect the detection accuracy at the CH and the energy consumption to achieve this level of accuracy. Correlated Random Walks (CRWs) have been proposed as mobility models that accounts for time dependency, geographical restrictions and nonzero drift. Hence, the solution to the continuous-time, 1-D, finite state space CRW is provided and its statistical behavior is studied both analytically and numerically. The impact of the motion of sensor on the network's performance is also studied.

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Xiu, Wanjing. "FAULT LOCATION ALGORITHMS, OBSERVABILITY AND OPTIMALITY FOR POWER DISTRIBUTION SYSTEMS." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/48.

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Power outages usually lead to customer complaints and revenue losses. Consequently, fast and accurate fault location on electric lines is needed so that repair work can be carried out as fast as possible. Chapter 2 describes novel fault location algorithms for radial and non-radial ungrounded power distribution systems. For both types of systems, fault location approaches using line to neutral or line to line measurements are presented. It’s assumed that network structure and parameters are known, so that during-fault bus impedance matrix of the system can be derived. Functions of bus impedance matrix and available measurements at substation are formulated, from which the unknown fault location can be estimated. Evaluation studies on fault location accuracy and robustness of fault location methods to load variations and measurement errors has been performed. Most existing fault location methods rely on measurements obtained from meters installed in power systems. To get the most from a limited number of meters available, optimal meter placement methods are needed. Chapter 3 presents a novel optimal meter placement algorithm to keep the system observable in terms of fault location determination. The observability of a fault location in power systems is defined first. Then, fault location observability analysis of the whole system is performed to determine the least number of meters needed and their best locations to achieve fault location observability. Case studies on fault location observability with limited meters are presented. Optimal meter deployment results based on the studied system with equal and varying monitoring cost for meters are displayed. To enhance fault location accuracy, an optimal fault location estimator for power distribution systems with distributed generation (DG) is described in Chapter 4. Voltages and currents at locations with power generation are adopted to give the best estimation of variables including measurements, fault location and fault resistances. Chi-square test is employed to detect and identify bad measurement. Evaluation studies are carried out to validate the effectiveness of optimal fault location estimator. A set of measurements with one bad measurement is utilized to test if a bad data can be identified successfully by the presented method.

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Schiavon, Francesca <1984&gt. "An optimal estimator for the correlation of CMB anisotropies with Large Scale Structures and its application to WMAP-7year and NVSS." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4793/1/schiavon_francesca_tesi.pdf.

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In the thesis we present the implementation of the quadratic maximum likelihood (QML) method, ideal to estimate the angular power spectrum of the cross-correlation between cosmic microwave background (CMB) and large scale structure (LSS) maps as well as their individual auto-spectra. Such a tool is an optimal method (unbiased and with minimum variance) in pixel space and goes beyond all the previous harmonic analysis present in the literature. We describe the implementation of the QML method in the {\it BolISW} code and demonstrate its accuracy on simulated maps throughout a Monte Carlo. We apply this optimal estimator to WMAP 7-year and NRAO VLA Sky Survey (NVSS) data and explore the robustness of the angular power spectrum estimates obtained by the QML method. Taking into account the shot noise and one of the systematics (declination correction) in NVSS, we can safely use most of the information contained in this survey. On the contrary we neglect the noise in temperature since WMAP is already cosmic variance dominated on the large scales. Because of a discrepancy in the galaxy auto spectrum between the estimates and the theoretical model, we use two different galaxy distributions: the first one with a constant bias $b$ and the second one with a redshift dependent bias $b(z)$. Finally, we make use of the angular power spectrum estimates obtained by the QML method to derive constraints on the dark energy critical density in a flat $\Lambda$CDM model by different likelihood prescriptions. When using just the cross-correlation between WMAP7 and NVSS maps with 1.8° resolution, we show that $\Omega_\Lambda$ is about the 70\% of the total energy density, disfavouring an Einstein-de Sitter Universe at more than 2 $\sigma$ CL (confidence level).

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Schiavon, Francesca <1984&gt. "An optimal estimator for the correlation of CMB anisotropies with Large Scale Structures and its application to WMAP-7year and NVSS." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4793/.

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In the thesis we present the implementation of the quadratic maximum likelihood (QML) method, ideal to estimate the angular power spectrum of the cross-correlation between cosmic microwave background (CMB) and large scale structure (LSS) maps as well as their individual auto-spectra. Such a tool is an optimal method (unbiased and with minimum variance) in pixel space and goes beyond all the previous harmonic analysis present in the literature. We describe the implementation of the QML method in the {\it BolISW} code and demonstrate its accuracy on simulated maps throughout a Monte Carlo. We apply this optimal estimator to WMAP 7-year and NRAO VLA Sky Survey (NVSS) data and explore the robustness of the angular power spectrum estimates obtained by the QML method. Taking into account the shot noise and one of the systematics (declination correction) in NVSS, we can safely use most of the information contained in this survey. On the contrary we neglect the noise in temperature since WMAP is already cosmic variance dominated on the large scales. Because of a discrepancy in the galaxy auto spectrum between the estimates and the theoretical model, we use two different galaxy distributions: the first one with a constant bias $b$ and the second one with a redshift dependent bias $b(z)$. Finally, we make use of the angular power spectrum estimates obtained by the QML method to derive constraints on the dark energy critical density in a flat $\Lambda$CDM model by different likelihood prescriptions. When using just the cross-correlation between WMAP7 and NVSS maps with 1.8° resolution, we show that $\Omega_\Lambda$ is about the 70\% of the total energy density, disfavouring an Einstein-de Sitter Universe at more than 2 $\sigma$ CL (confidence level).

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El, Heda Khadijetou. "Choix optimal du paramètre de lissage dans l'estimation non paramétrique de la fonction de densité pour des processus stationnaires à temps continu." Thesis, Littoral, 2018. http://www.theses.fr/2018DUNK0484/document.

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Les travaux de cette thèse portent sur le choix du paramètre de lissage dans le problème de l'estimation non paramétrique de la fonction de densité associée à des processus stationnaires ergodiques à temps continus. La précision de cette estimation dépend du choix de ce paramètre. La motivation essentielle est de construire une procédure de sélection automatique de la fenêtre et d'établir des propriétés asymptotiques de cette dernière en considérant un cadre de dépendance des données assez général qui puisse être facilement utilisé en pratique. Cette contribution se compose de trois parties. La première partie est consacrée à l'état de l'art relatif à la problématique qui situe bien notre contribution dans la littérature. Dans la deuxième partie, nous construisons une méthode de sélection automatique du paramètre de lissage liée à l'estimation de la densité par la méthode du noyau. Ce choix issu de la méthode de la validation croisée est asymptotiquement optimal. Dans la troisième partie, nous établissons des propriétés asymptotiques, de la fenêtre issue de la méthode de la validation croisée, données par des résultats de convergence presque sûre<br>The work this thesis focuses on the choice of the smoothing parameter in the context of non-parametric estimation of the density function for stationary ergodic continuous time processes. The accuracy of the estimation depends greatly on the choice of this parameter. The main goal of this work is to build an automatic window selection procedure and establish asymptotic properties while considering a general dependency framework that can be easily used in practice. The manuscript is divided into three parts. The first part reviews the literature on the subject, set the state of the art and discusses our contribution in within. In the second part, we design an automatical method for selecting the smoothing parameter when the density is estimated by the Kernel method. This choice stemming from the cross-validation method is asymptotically optimal. In the third part, we establish an asymptotic properties pertaining to consistency with rate for the resulting estimate of the window-width

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Vollant, Antoine. "Evaluation et développement de modèles sous-maille pour la simulation des grandes échelles du mélange turbulent basés sur l'estimation optimale et l'apprentissage supervisé." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAI118/document.

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Dans ce travail, des méthodes de diagnostics et des techniques de développement de modèles sous-maille sont proposées pour la simulation des grandes échelles (SGE) du mélange turbulent. Plusieurs modèles sous-maille issus de ces stratégies sont ainsi présentés pour illustrer ces méthodes.Le principe de la SGE est de résoudre les grandes échelles de l'écoulement responsables des transferts principaux et de modéliser l'action des petites échelles de l'écoulement sur les échelles résolues. Au cours de ce travail, nous nous sommes appuyés sur le classement des modèles sous-maille en deux catégories. Les modèles "fonctionnels" qui s'attachent à reproduire les transferts énergétiques entre les échelles résolues et les échelles modélisées et les modèles "structurels" qui cherchent à bien reproduire le terme sous-maille. Le premier enjeu important a été d'évaluer la performance des modèles sous-maille en prenant en compte leur comportement à la fois fonctionnel (capacité à reproduire les transferts d'énergie) et structurel (capacité à reproduire le terme sous-maille exact). Des diagnosctics des modèles sous-maille ont pu être conduits avec l'utilisation de la notion d'estimateur optimal ce qui permet de connaitre le potentiel d'amélioration structurelle des modèles. Ces principes ont dans un premier temps servi au développement d'une première famille de modèles sous-maille algébrique appelée DRGM pour "Dynamic Regularized Gradient Model". Cette famille de modèles s'appuie sur le diagnostic structurel des termes issus de la régularisation des modèles de la famille du gradient. D'après les tests menés, cette nouvelle famille de modèle structurel a de meilleures performances fonctionnelles et structurelles que les modèles de la famille du gradient. L'amélioration des performances fonctionnelles consiste à supprimer la prédiction excessive de transferts inverses d'énergie (backscatter) observés dans les modèles de la famille du gradient. Cela permet ainsi de supprimer le comportement instable classiquement observé pour cette famille de modèles. La suite de ce travail propose ensuite d'utiliser l'estimateur optimal directement comme modèle sous-maille. Comme l'estimateur optimal fournit le modèle ayant la meilleure performance structurelle pour un jeu de variables donné, nous avons recherché le jeu de variable optimisant cette performance. Puisque ce jeu comporte un nombre élevé de variables, nous avons utilisé les fonctions d'approximation de type réseaux de neurones pour estimer cet estimateur optimal. Ce travail a mené au nouveau modèle substitut ANNM pour "Artificial Neural Network Model". Ces fonctions de substitution se construisent à partir de bases de données servant à émuler les termes exacts nécessaire à la détermination de l'estimateur optimal. Les tests de ce modèle ont montré qu'il avait de très bonnes perfomances pour des configurations de simulation peu éloignées de la base de données servant à son apprentissage, mais qu'il pouvait manquer d'universalité. Pour lever ce dernier verrou, nous avons proposé une utilisation hybride des modèles algébriques et des modèles de substitution à base de réseaux de neurones. La base de cette nouvelle famille de modèles ACM pour "Adaptative Coefficient Model" s'appuie sur les décompositions vectorielles et tensorielles des termes sous-maille exacts. Ces décompositions nécessitent le calcul de coefficients dynamiques qui sont modélisés par les réseaux de neurones. Ces réseaux bénéficient d'une méthode d'apprentissage permettant d'optimiser directement les performances structurelles et fonctionnelles des modèles ACM. Ces modèles hybrides allient l'universalité des modèles algébriques avec la performance élevée mais spécialisée des fonctions de substitution. Le résultat conduit à des modèles plus universels que l'ANNM<br>This work develops subgrid model techniques and proposes methods of diagnosis for Large Eddy Simulation (LES) of turbulent mixing.Several models from these strategies are thus presented to illustrate these methods.The principle of LES is to solve the largest scales of the turbulent flow responsible for major transfers and to model the action of small scales of flowon the resolved scales. Formally, this operation leads to filter equations describing turbulent mixing. Subgrid terms then appear and must bemodeled to close the equations. In this work, we rely on the classification of subgrid models into two categories. "Functional" models whichreproduces the energy transfers between the resolved scales and modeled scales and "Structural" models that seek to reproduce the exact subgrid termitself. The first major challenge is to evaluate the performance of subgrid models taking into account their functional behavior (ability to reproduce theenergy transfers) and structural behaviour (ability to reproduce the term subgrid exactly). Diagnostics of subgrid models have been enabled with theuse of the optimal estimator theory which allows the potential of structural improvement of the model to be evaluated.These methods were initially involved for the development of a first family of models called algebraic subgrid $DRGM$ for "Dynamic Regularized GradientModel". This family of models is based on the structural diagnostic of terms given by the regularization of the gradient model family.According to the tests performed, this new structural model's family has better functional and structural performance than original model's family of thegradient. The improved functional performance is due to the vanishing of inverse energy transfer (backscatter) observed in models of thegradient family. This allows the removal of the unstable behavior typically observed for this family of models.In this work, we then propose the use of the optimal estimator directly as a subgrid scale model. Since the optimal estimator provides the modelwith the best structural performance for a given set of variables, we looked for the set of variables which optimize that performance. Since this set of variablesis large, we use surrogate functions of artificial neural networks type to estimate the optimal estimator. This leads to the "Artificial Neural Network Model"(ANNM). These alternative functions are built from databases in order to emulate the exact terms needed to determine the optimal estimator. The tests of this modelshow that he it has very good performance for simulation configurations not very far from its database used for learning, so these findings may fail thetest of universality.To overcome this difficulty, we propose a hybrid method using an algebraic model and a surrogate model based on artificial neural networks. Thebasis of this new model family $ACM$ for "Adaptive Coefficient Model" is based on vector and tensor decomposition of the exact subgrid terms. Thesedecompositions require the calculation of dynamic coefficients which are modeled by artificial neural networks. These networks have a learning method designedto directlyoptimize the structural and functional performances of $ACM$. These hybrids models combine the universality of algebraic model with high performance butvery specialized performance of surrogate models. The result give models which are more universal than ANNM

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Teixeira, Marcos Vinícius. "Estudos sobre a implementação online de uma técnica de estimação de energia no calorímetro hadrônico do atlas em cenários de alta luminosidade." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/4169.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-04-25T13:40:30Z No. of bitstreams: 1 marcosviniciusteixeira.pdf: 5877294 bytes, checksum: 8fe056549285d49782c2d9ec8e16f786 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-04-25T15:26:43Z (GMT) No. of bitstreams: 1 marcosviniciusteixeira.pdf: 5877294 bytes, checksum: 8fe056549285d49782c2d9ec8e16f786 (MD5)<br>Made available in DSpace on 2017-04-25T15:26:43Z (GMT). No. of bitstreams: 1 marcosviniciusteixeira.pdf: 5877294 bytes, checksum: 8fe056549285d49782c2d9ec8e16f786 (MD5) Previous issue date: 2015-08-21<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Este trabalho tem como objetivo o estudo de técnicas para a estimação da amplitude de sinais no calorímetro de telhas (TileCal) do ATLAS no LHC em cenários de alta luminosidade. Em alta luminosidade, sinais provenientes de colisões adjacentes são observados, ocasionando o efeito de empilhamento de sinais. Neste ambiente, o método COF (do inglês, Constrained Optimal Filter), apresenta desempenho superior ao algoritmo atualmente implementado no sistema. Entretanto, o COF requer a inversão de matrizes para o cálculo da pseudo-inversa de uma matriz de convolução, diﬁcultando sua implementação online. Para evitar a inversão de matrizes, este trabalho apresenta métodos interativos, para a daptação do COF, que resultam em operações matemáticas simples. Baseados no Gradiente Descendente, os resultados demonstraram que os algoritmos são capazes de estimar a amplitude de sinais empilhados, além do sinal de interesse com eﬁciência similar ao COF. Visando a implementação online, este trabalho apresenta estudos sobre a complexidade dos métodos iterativos e propõe uma arquitetura de processamento em FPGA. Baseado em uma estrutura sequencial e utilizando lógica aritmética em ponto ﬁxo, os resultados demonstraram que a arquitetura desenvolvida é capaz executar o método iterativo, atendendo os requisitos de tempo de processamento exigidos no TileCal.<br>This work aims at the study of techniques for online energy estimation in the ATLAS hadronic Calorimeter (TileCal) on the LHC collider. During further periods of the LHC operation, signals coming from adjacent collisions will be observed within the same window, producing a signal superposition. In this environment, the energy reconstruction method COF (Constrained Optimal Filter) outperforms the algorithm currently implemented in the system. However , the COF method requires an inversion of matrices and its online implementation is not feasible. To avoid such inversion of matrices, this work presents iteractive methods to implement the COF, resulting in simple mathematical operations. Based on the Gradient Descent, the results demonstrate that the algorithms are capable of estimating the amplitude of the superimposed signals with eﬃciency similar to COF. In addition, a processing architecture for FPGA implementation is proposed. The analysis has shown that the algorithms can be implemented in the new TilaCal electronics, reaching the processing time requirements.

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Bringmann, Philipp. "Adaptive least-squares finite element method with optimal convergence rates." Doctoral thesis, Humboldt-Universität zu Berlin, 2021. http://dx.doi.org/10.18452/22350.

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Die Least-Squares Finite-Elemente-Methoden (LSFEMn) basieren auf der Minimierung des Least-Squares-Funktionals, das aus quadrierten Normen der Residuen eines Systems von partiellen Differentialgleichungen erster Ordnung besteht. Dieses Funktional liefert einen a posteriori Fehlerschätzer und ermöglicht die adaptive Verfeinerung des zugrundeliegenden Netzes. Aus zwei Gründen versagen die gängigen Methoden zum Beweis optimaler Konvergenzraten, wie sie in Carstensen, Feischl, Page und Praetorius (Comp. Math. Appl., 67(6), 2014) zusammengefasst werden. Erstens scheinen fehlende Vorfaktoren proportional zur Netzweite den Beweis einer schrittweisen Reduktion der Least-Squares-Schätzerterme zu verhindern. Zweitens kontrolliert das Least-Squares-Funktional den Fehler der Fluss- beziehungsweise Spannungsvariablen in der H(div)-Norm, wodurch ein Datenapproximationsfehler der rechten Seite f auftritt. Diese Schwierigkeiten führten zu einem zweifachen Paradigmenwechsel in der Konvergenzanalyse adaptiver LSFEMn in Carstensen und Park (SIAM J. Numer. Anal., 53(1), 2015) für das 2D-Poisson-Modellproblem mit Diskretisierung niedrigster Ordnung und homogenen Dirichlet-Randdaten. Ein neuartiger expliziter residuenbasierter Fehlerschätzer ermöglicht den Beweis der Reduktionseigenschaft. Durch separiertes Markieren im adaptiven Algorithmus wird zudem der Datenapproximationsfehler reduziert. Die vorliegende Arbeit verallgemeinert diese Techniken auf die drei linearen Modellprobleme das Poisson-Problem, die Stokes-Gleichungen und das lineare Elastizitätsproblem. Die Axiome der Adaptivität mit separiertem Markieren nach Carstensen und Rabus (SIAM J. Numer. Anal., 55(6), 2017) werden in drei Raumdimensionen nachgewiesen. Die Analysis umfasst Diskretisierungen mit beliebigem Polynomgrad sowie inhomogene Dirichlet- und Neumann-Randbedingungen. Abschließend bestätigen numerische Experimente mit dem h-adaptiven Algorithmus die theoretisch bewiesenen optimalen Konvergenzraten.<br>The least-squares finite element methods (LSFEMs) base on the minimisation of the least-squares functional consisting of the squared norms of the residuals of first-order systems of partial differential equations. This functional provides a reliable and efficient built-in a posteriori error estimator and allows for adaptive mesh-refinement. The established convergence analysis with rates for adaptive algorithms, as summarised in the axiomatic framework by Carstensen, Feischl, Page, and Praetorius (Comp. Math. Appl., 67(6), 2014), fails for two reasons. First, the least-squares estimator lacks prefactors in terms of the mesh-size, what seemingly prevents a reduction under mesh-refinement. Second, the first-order divergence LSFEMs measure the flux or stress errors in the H(div) norm and, thus, involve a data resolution error of the right-hand side f. These difficulties led to a twofold paradigm shift in the convergence analysis with rates for adaptive LSFEMs in Carstensen and Park (SIAM J. Numer. Anal., 53(1), 2015) for the lowest-order discretisation of the 2D Poisson model problem with homogeneous Dirichlet boundary conditions. Accordingly, some novel explicit residual-based a posteriori error estimator accomplishes the reduction property. Furthermore, a separate marking strategy in the adaptive algorithm ensures the sufficient data resolution. This thesis presents the generalisation of these techniques to three linear model problems, namely, the Poisson problem, the Stokes equations, and the linear elasticity problem. It verifies the axioms of adaptivity with separate marking by Carstensen and Rabus (SIAM J. Numer. Anal., 55(6), 2017) in three spatial dimensions. The analysis covers discretisations with arbitrary polynomial degree and inhomogeneous Dirichlet and Neumann boundary conditions. Numerical experiments confirm the theoretically proven optimal convergence rates of the h-adaptive algorithm.

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Ngwenya, Mzabalazo Z. "Investigating 'optimal' kriging variance estimation :analytic and bootstrap estimators." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11265.

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Kriging is a widely used group of techniques for predicting unobserved responses at specified locations using a set of observations obtained from known locations. Kriging predictors are best linear unbiased predictors (BLUPs) and the precision of predictions obtained from them are assessed by the mean squared prediction error (MSPE), commonly termed the kriging variance.

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Books on the topic "Optimal estimator"

1

Simon, Dan. Optimal State Estimation. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470045345.

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Simon, Dan. Optimal State Estimation. John Wiley & Sons, Ltd., 2006.

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Kamen, E. W., and J. K. Su. Introduction to Optimal Estimation. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0417-9.

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Chen, Youyi. Extended quasi-likelihoods and optimal estimating functions. University of Toronto, Dept. of Statistics, 1991.

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Crassidis, John L. Optimal estimation of dynamic systems. Chapman & Hall/CRC, 2004.

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L, Junkins John, ed. Optimal estimation of dynamic systems. 2nd ed. Chapman and Hall/CRC, 2011.

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Malley, James D. Optimal Unbiased Estimation of Variance Components. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2.

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Feder, Meir. Optimal multiple source location via the EM algorithm. Woods Hole Oceanographic Institution, 1986.

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Cheung, Man-Fung. On optimal algorithms for parameter set estimation. Ohio State University, 1991.

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Kuersteiner, Guido M. Optimal instrumental variables estimation for ARMA models. Dept. of Economics, Massachusetts Institute of Technology, 1999.

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Book chapters on the topic "Optimal estimator"

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Lin, Hong, Hongye Su, Peng Shi, Zhan Shu, and Zheng-Guang Wu. "Stability of Optimal Estimator for UDP-Like Systems." In Studies in Systems, Decision and Control. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44212-9_4.

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Nangsue, Nuanpan, and Yves G. Berger. "Optimal Regression Estimator for Stratified Two-Stage Sampling." In Contributions to Statistics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05320-2_11.

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Xia, Yingcun, Wolfgang Karl Härdle, and Oliver Linton. "Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator." In Exploring Research Frontiers in Contemporary Statistics and Econometrics. Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2349-3_11.

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Janet, Akingbade Toluwalase, and Balogun Oluwafemi Samson. "Class of Exponential Ratio Type Estimator for Population Mean in Adaptive Cluster Sampling." In Optimal Decision Making in Operations Research and Statistics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003106951-12.

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Park, Suk Yung, and Arthur D. Kuo. "An Optimal Estimator Model of Multi-Sensory Processing in Human Postural Control." In Key Engineering Materials. Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-958-x.148.

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Arias, Roberto, Stephen A. Sedory, and Sarjinder Singh. "Modified Regression Type Estimator by Ingeniously Utilizing Probabilities for more Efficient Results in Randomized Response Sampling." In Optimal Decision Making in Operations Research and Statistics. CRC Press, 2021. http://dx.doi.org/10.1201/9781003106951-10.

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Fetecǎu, Grigore, Viorel Nicolau, Vasile Palade, and Maria Fetecǎu. "Intelligent Optimal Control of a Biosynthesis Process Using a Neural Network Based Estimator." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45226-3_129.

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Demin, Victor, and Ekaterina Chimitova. "A Method for Selection of the Optimal Bandwidth Parameter for Beran’s Nonparametric Estimator." In Springer Proceedings in Mathematics & Statistics. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2104-1_13.

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Bassiakos, Y. "Optimal Choice for a Hodges-Lehmann Type Estimator in the Two-Sample Problem with Censoring." In Computational Statistics. Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-642-48678-4_8.

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Heyde, C. C. "On an Optimal Asymptotic Property of the Maximum Likelihood Estimator of a Parameter from a Stochastic Process." In Selected Works of C.C. Heyde. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5823-5_44.

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Conference papers on the topic "Optimal estimator"

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Zhao, Mengyuan, Tobias J. Oechtering, and Maël Le Treust. "Optimal Gaussian Strategies for Vector-valued Witsenhausen Counterexample with Non-causal State Estimator." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886216.

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Cottis, R. A. "An Evaluation of Electrochemical Noise for the Estimation of Corrosion Rate and Type." In CORROSION 2006. NACE International, 2006. https://doi.org/10.5006/c2006-06432.

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Abstract Electrochemical noise (the spontaneous fluctuations in current or potential associated with corroding electrodes) has been studied for about 37 years, but it is only relatively recently that a reasonably sound theoretical basis for the technique has been derived. It is now clear that the technique provides a method, the determination of electrochemical noise resistance (Rn) that provides a reasonable estimate of the corrosion rate. It also seems probable that it is able to provide information about the type of corrosion occurring, although the optimal technique for doing this is not yet established. Furthermore, there are indications that although the estimate of corrosion rate is relatively robust, indicators of corrosion type are very susceptible to extraneous influences. However, as an estimator of corrosion rate, electrochemical noise measurement is relatively poor compared to conventional techniques, but its potential ability to identify the type of corrosion occurring remains its major advantage.

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Karsaz, A., H. Khaloozadeh, and M. Darbandi. "Optimal Partitioned State Kalman Estimator." In 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA 2010). IEEE, 2010. http://dx.doi.org/10.1109/isscaa.2010.5633606.

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Dani, Ashwin, and Nitin Sharma. "A Discrete-Time Nonlinear Estimator for an Orthosis-Aided Gait." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6161.

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To achieve automatic operation of a powered orthosis-aided gait or functional electrical stimulation-based walking restoration, accurate estimation of the leg angles is of utmost importance. Various phases of walking last for a short duration of time; thus, an accurate estimator is required with a fast convergence rate. To overcome this challenge, this paper presents a discrete-time nonlinear estimation algorithm to estimate lower-limb angles during an orthosis-aided walking. To this end, we use measurements from 6 degree-of-freedom (DOF) inertial measurement units (IMUs) to estimate the lower limb angles. The estimator is based on a state-dependent coefficient (SDC) linearization or extended linearization of the nonlinear functions. A combination of multiple discrete SDCs is used to compute an optimal gain of the nonlinear estimator based on uncertainty minimization criteria. The nonlinear estimator is robust to uncertainties in system modeling and sensor noise/bias from the IMUs. Monte Carlo simulation studies reveal that the estimator outperforms widely used discrete-time extended Kalman (EKF) filter with respect to average root-mean squared estimation error (RMSE) criteria.

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Kiriakidis, Kiriakos, and Richard O'Brien. "Optimal Estimation of Blood Insulin From Blood Glucose." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14776.

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Plasma insulin estimation from plasma glucose has been proposed in order to avoid hyperinsulinemia in the control of diabetes. This paper presents an estimator with error feedback based on measured and predicted plasma glucose designed to tolerate measurement noise as well as discretization error by means of the H∞ criterion. The proposed estimator is tested and evaluated using synthetic patient data.

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Kewu Peng, Aolin Xu, and Zhixing Yang. "Optimal correlation based frequency estimator with maximal estimation range." In 2008 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2008. http://dx.doi.org/10.1109/icccas.2008.4657772.

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Li, Chang, and Roger Fales. "Application of Extended Kalman Filter in a Forced-Feedback Metering Poppet Valve System." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2247.

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This work focuses on an accurate Extended Kalman Filter (EKF) estimator, which is applied in a forced-feedback metering poppet valve system (FFMPVS). The EKF estimator is used to estimate the position and velocity of the main poppet valve, the pilot poppet valve and the piston through using the control volume pressure, the load pressure and the pressure between the pilot poppet and the actuator housing, which are all disturbed by noise. The EKF estimator takes advantage of its recursive optimal state estimation to estimate the states of this metering poppet valve, which is a non-linear, time-variant dynamical system in real time. The EKF estimator has robustness to parameter variations and ability to filter measurement noises. It is shown that the EKF estimator tracks the states confidently and promptly for both the steady-state and transient performance, at the same time, the EKF estimator also filters the noise of the measured pressures.

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Schuster, Guido M., and Aggelos K. Katsaggelos. "Optimal quad-tree-based motion estimator." In Advanced Imaging and Network Technologies, edited by Naohisa Ohta. SPIE, 1996. http://dx.doi.org/10.1117/12.251317.

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Kasdin, N. Jeremy, and Thomas Weaver. "Recursive Satellite Attitude Estimation with the Two-Step Optimal Estimator." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-4462.

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Chen, Tao, Lei Wang, Zhitao Liu, Wenhai Wang, and Hongye Su. "Optimal Stealthy Attack to Remote Estimator for Estimation Error Regulation." In 2023 American Control Conference (ACC). IEEE, 2023. http://dx.doi.org/10.23919/acc55779.2023.10155923.

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Reports on the topic "Optimal estimator"

1

Imbens, Guido, and Karthik Kalyanaraman. Optimal Bandwidth Choice for the Regression Discontinuity Estimator. National Bureau of Economic Research, 2009. http://dx.doi.org/10.3386/w14726.

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Imbens, Guido, and Karthik Kalyanaraman. Optimal bandwidth choice for the regression discontinuity estimator. Institute for Fiscal Studies, 2010. http://dx.doi.org/10.1920/wp.cem.2010.0510.

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Arai, Yoichi, and Hidehiko Ichimura. Optimal bandwidth selection for the fuzzy regression discontinuity estimator. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.4915.

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Basak, Gopal, Ravi Jagannathan, and Tongshu Ma. A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10447.

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Ichimura, Hidehiko, and Yoichi Arai. Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.4215.

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Eichel, P. H. The phase gradient autofocus algorithm: An optimal estimator of the phase derivative. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5609345.

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regression models. We provide a host of theoretical results and practical tools for local constant estimation along with piecewise polynomial and spline approximations, including (i) optimal tuning parameter (number of bins) selection, (ii) confidence bands, and (iii) formal statistical tests regarding functional form or shape restrictions. Our main results rely on novel strong approximations for general partitioning-based estimators covering random, data-driven partitions, which may be of independent interest. We demonstrate our methods with an empirical application studying the relation between the percentage of individuals without health insurance and per capita income at the zip-code level. We provide general-purpose software packages implementing our methods in Python, R, and Stata.

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Zanoni, Wladimir, and Ailin He. Citizenship and the Economic Assimilation of Canadian Immigrants. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003117.

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In this paper, we examine whether acquiring citizenship improves the economic assimilation of Canadian migrants. We took advantage of a natural experiment made possible through changes in the Canadian Citizenship Act of 2014, which extended the physical presence requirement for citizenship from three to four years. Using quasi-experimental methods, we found that delaying citizenship eligibility by one year adversely affected Canadian residents' wages. Access to better jobs explains a citizenship premium of 11 percent in higher wages among naturalized migrants. Our estimates are robust to model specifications, differing sampling windows to form the treatment and comparison groups, and whether the estimator is a non-parametric rather than a parametric one. We discuss how our findings are relevant to the optimal design of naturalization policies regarding efficiency and equity.

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Dufour-Simard, Xavier, Pierre-Carl Michaud, and Michael Smart. Is the elasticity of taxable income mostly an income effect ? CIRANO, 2025. https://doi.org/10.54932/xpae6815.

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We use variation in marginal tax rates and in tax bracket thresholds at which they apply in order to identify the substitution and income effects of tax reforms. We use a triple-difference estimator that exploits variation from subnational tax reforms, for which behavioral responses to taxes are identified e ven i n t he p r esence o f unobservable shocks to the income distribution. While high-income taxpayers respond more to tax changes, our results suggest this reflects much more the income or salience effects of tax reforms, rather than inherent heterogeneity in substitution effects. We discuss the implications for optimal redistributive tax policies.

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Lau, Herbert, Tsai-Hong Hong, and Martin Herman. Optimal estimation of optical flow, time-to-contact and depth. National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4919.

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