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Academic literature on the topic 'Least-Square estimator'

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Published: 3 May 2025

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Journal articles on the topic "Least-Square estimator"

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Kwon, Bokyu, and Soohee Han. "Least-Mean-Square Receding Horizon Estimation." Mathematical Problems in Engineering 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/631759.

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We propose a least-mean-square (LMS) receding horizon (RH) estimator for state estimation. The proposed LMS RH estimator is obtained from the conditional expectation of the estimated state given a finite number of inputs and outputs over the recent finite horizon. Anya prioristate information is not required, and existing artificial constraints for easy derivation are not imposed. For a general stochastic discrete-time state space model with both system and measurement noise, the LMS RH estimator is explicitly represented in a closed form. For numerical reliability, the iterative form is presented with forward and backward computations. It is shown through a numerical example that the proposed LMS RH estimator has better robust performance than conventional Kalman estimators when uncertainties exist.

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Zulkifli, Raudhah, Nazim Aimran, Sayang Mohd Deni, and Fatin Najihah Badarisam. "A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling." International Journal of Data and Network Science 6, no. 2 (2022): 391–400. http://dx.doi.org/10.5267/j.ijdns.2021.12.015.

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Structural equation modeling offers various estimation methods for estimating parameters. The most used method in covariance-based structural equation modeling (CB-SEM) is the maximum likelihood (ML) estimator. The ML estimator is typically used when fitting models with normally distributed data. The growth of partial least squares path modeling (PLS-PM), including consistent partial least squares (PLSc), has also been noticed by researchers in the SEM fields. The PLSc has elevated interest in the scholastic setting in measuring the performance of various estimation methods in structural equation modeling. The choice of estimation methods has substantial impact in yielding parameter estimates. There could be a trade-off among the estimation methods’ ability to deal with different types of data based on the model tested. Accordingly, this study aims to compare the performance of ML, generalized least squares (GLS), and scale-free least squares (SFLS) for CB-SEM as well as partial least squares (PLS) and consistent partial least squares (PLSc). Multivariate normal data were generated using Monte Carlo simulation with pre-determined population parameters and sample sizes using R Programming packages. To produce the estimated values, data analysis was performed using AMOS and SmartPLS for CB-SEM and PLS-SEM, respectively. The findings illustrate notable similarities between CB-SEM (ML) and PLS-SEM results when the true indicator loading is certainly high.

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Setiawan, Ezra Putranda, and Dedi Rosadi. "APPLICATION OF ROBUST REGRESSION FOR PORTFOLIO OPTIMIZATION." Matrix Science Mathematic 7, no. 1 (January 5, 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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Abdi, Hamdan, Sajaratud Dur, Rina Widyasar, and Ismail Husein. "Analysis of Efficiency of Least Trimmed Square and Least Median Square Methods in The Estimation of Robust Regression Parameters." ZERO: Jurnal Sains, Matematika dan Terapan 4, no. 1 (August 16, 2020): 21. http://dx.doi.org/10.30829/zero.v4i1.7933.

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<span lang="EN">Robust regression is a regression method used when the remainder's distribution is not reasonable, or there is an outreach to observational data that affects the model. One method for estimating regression parameters is the Least Squares Method (MKT). The method is easily affected by the presence of outliers. Therefore we need an alternative method that is robust to the presence of outliers, namely robust regression. Methods for estimating robust regression parameters include Least Trimmed Square (LTS) and Least Median Square (LMS). These methods are estimators with high breakdown points for outlier observational data and have more efficient algorithms than other estimation methods. This study aims to compare the regression models formed from the LTS and LMS methods, determine the efficiency of the model formed, and determine the factors that influence the production of community oil palm in Langkat District in 2018. The results showed that in testing, the estimated model of the regression parameters showed the same results. Compared to the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018</span>

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SÖKÜT AÇAR, Tuğba. "Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation." Journal of New Theory, no. 41 (December 31, 2022): 1–17. http://dx.doi.org/10.53570/jnt.1139885.

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The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical literature. The recently proposed Kibria-Lukman estimator is one of the Ridge-type estimators. The literature has compared the Kibria-Lukman estimator with the others using the mean square error criterion for the linear regression model. It was achieved in a study conducted on the Kibria-Lukman estimator's performance under the first-order autoregressive erroneous autocorrelation. When there is an autocorrelation problem with the second-order, evaluating the performance of the Kibria-Lukman estimator according to the mean square error criterion makes this paper original. The scalar mean square error of the Kibria-Lukman estimator under the second-order autoregressive error structure was evaluated using a Monte Carlo simulation and two real examples, and compared with the Generalized Least-squares, Ridge, and Liu estimators. The findings revealed that when the variance of the model was small, the mean square error of the Kibria-Lukman estimator gave very close values with the popular biased estimators. As the model variance grew, Kibria-Lukman did not give fairly similar values with popular biased estimators as in the model with small variance. However, according to the mean square error criterion the Kibria-Lukman estimator outperformed the Generalized Least-Squares estimator in all possible cases.

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Mohmadishak Sheikh, Chetan Sheth. "System State Estimation Using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 1294–99. http://dx.doi.org/10.52783/cienceng.v11i1.276.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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Chetan Sheth, Mohmadishak Sheikh,. "Power System State Estimation using Weighted Least Square Method." Proceeding International Conference on Science and Engineering 11, no. 1 (February 18, 2023): 1721–27. http://dx.doi.org/10.52783/cienceng.v11i1.327.

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State estimation is an essential part of every energy control management system. Accurate estimation of state or operating state is essential for security control and monitoring of power systems. Power system state estimation is a procedure to estimate true state from the inexact state of a power system. The conventional state estimator provides estimates of the power system states, i.e., bus voltages and angles which is obtained. State estimation is a computational technique for electrical power system. It empowers the calculation of the power flows of the electrical power system which are not observed or not directly measured. State estimation is a computer program that detects, isolate and eliminate the incorrect or bad measurement data and estimates the accurate state. The magnitudes of bus voltage and phase angle are the states variables for an electrical power system. This paper outlines Weighted Least Square (WLS) estimation techniques and simulated estimation for standard IEEE systems.

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Aladeitan, BENEDICTA, Adewale F. Lukman, Esther Davids, Ebele H. Oranye, and Golam B. M. Kibria. "Unbiased K-L estimator for the linear regression model." F1000Research 10 (August 19, 2021): 832. http://dx.doi.org/10.12688/f1000research.54990.1.

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Background: In the linear regression model, the ordinary least square (OLS) estimator performance drops when multicollinearity is present. According to the Gauss-Markov theorem, the estimator remains unbiased when there is multicollinearity, but the variance of its regression estimates become inflated. Estimators such as the ridge regression estimator and the K-L estimators were adopted as substitutes to the OLS estimator to overcome the problem of multicollinearity in the linear regression model. However, the estimators are biased, though they possess a smaller mean squared error when compared to the OLS estimator. Methods: In this study, we developed a new unbiased estimator using the K-L estimator and compared its performance with some existing estimators theoretically, simulation wise and by adopting real-life data. Results: Theoretically, the estimator even though unbiased also possesses a minimum variance when compared with other estimators. Results from simulation and real-life study showed that the new estimator produced smaller mean square error (MSE) and had the smallest mean square prediction error (MSPE). This further strengthened the findings of the theoretical comparison using both the MSE and the MSPE as criterion. Conclusions: By simulation and using a real-life application that focuses on modelling, the high heating values of proximate analysis was conducted to support the theoretical findings. This new method of estimation is recommended for parameter estimation with and without multicollinearity in a linear regression model.

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Adedia, David, Atinuke O. Adebanji, and Simon Kojo Appiah. "Comparative Analysis of Some Structural Equation Model Estimation Methods with Application to Coronary Heart Disease Risk." Journal of Probability and Statistics 2020 (September 22, 2020): 1–15. http://dx.doi.org/10.1155/2020/4181426.

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This study compared a ridge maximum likelihood estimator to Yuan and Chan (2008) ridge maximum likelihood, maximum likelihood, unweighted least squares, generalized least squares, and asymptotic distribution-free estimators in fitting six models that show relationships in some noncommunicable diseases. Uncontrolled hypertension has been shown to be a leading cause of coronary heart disease, kidney dysfunction, and other negative health outcomes. It poses equal danger when asymptomatic and undetected. Research has also shown that it tends to coexist with diabetes mellitus (DM), with the presence of DM doubling the risk of hypertension. The study assessed the effect of obesity, type II diabetes, and hypertension on coronary risk and also the existence of converse relationship with structural equation modelling (SEM). The results showed that the two ridge estimators did better than other estimators. Nonconvergence occurred for most of the models for asymptotic distribution-free estimator and unweighted least squares estimator whilst generalized least squares estimator had one nonconvergence of results. Other estimators provided competing outputs, but unweighted least squares estimator reported unreliable parameter estimates such as large chi-square test statistic and root mean square error of approximation for Model 3. The maximum likelihood family of estimators did better than others like asymptotic distribution-free estimator in terms of overall model fit and parameter estimation. Also, the study found that increase in obesity could result in a significant increase in both hypertension and coronary risk. Diastolic blood pressure and diabetes have significant converse effects on each other. This implies those who are hypertensive can develop diabetes and vice versa.

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Dey, Sanku, Mahendra Saha, and Sankar Goswami. "One Parameter A (α) Distribution: Different Methods of Estimation." Spectrum: Science and Technology 8, no. 1 (December 15, 2021): 01–09. http://dx.doi.org/10.54290/spect/2021.v8.1.0001.

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This paper addresses the different methods of estimation of the unknown parameter of one parameter A(α) distribution from the frequentist point of view. We briefly describe different approaches, namely, maximum likelihood estimator, least square and weighted least square estimators, maximum product spacing estimators, Cram´er-von Mises estimator and compare those using extensive numerical simulations. Next, we obtain parametric bootstrap confidence interval of the parameter using frequentist approaches. Finally, one real data set has been analysed for illustrative purposes.

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Dissertations / Theses on the topic "Least-Square estimator"

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Doheny, David A. "Real Time Digital Signal Processing Adaptive Filters for Correlated Noise Reduction in Ring Laser Gyro Inertial Systems." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000306.

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Zhang, Zongjun. "Adaptive Robust Regression Approaches in data analysis and their Applications." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445343114.

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Moda, Hari Priya. "Non-Negative Least Square Optimization Model for Industrial Peak Load Estimation." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/36003.

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Load research is the study of load characteristics on a power distribution system which helps planning engineer make decisions about equipment ratings and future expansion decisions. As it is expensive to collect and maintain data across the entire system, data is collected only for a sample of customers, where the sample is divided into groups based upon the customer class. These sample measurements are used to calculate the load research factors like kWHr-to-peak kW conversion factors, diversity factors and 24 hour average consumption as a function of class, month and day type. These factors are applied to the commonly available monthly billing kW data to estimate load on the system. Among various customers on a power system, industrial customers form an important group for study as their annual kWHr consumption is among the highest. Also the errors with which the estimates are calculated are also highest for this class. Hence we choose the industrial class to demonstrate the Lawson-Hanson Non-Negative Least Square (NNLS) optimization technique to minimize the residual squared error between the estimated loads and the SCADA currents on the system. Five feeders with industrial dominant customers are chosen to demonstrate the improvement provided by the NNLS model. The results showed significant improvement over the Nonlinear Load Research Estimation (NLRE) method.
Master of Science

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Mbah, Alfred Kubong. "On the theory of records and applications." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002216.

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Gaspard, Guetchine. "FLOOD LOSS ESTIMATE MODEL: RECASTING FLOOD DISASTER ASSESSMENT AND MITIGATION FOR HAITI, THE CASE OF GONAIVES." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1236.

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This study aims at developing a model to estimate flood damage cost caused in Gonaives, Haiti by Hurricane Jeanne in 2004. In order to reach this goal, the influence of income, inundation duration and inundation depth, slope, population density and distance to major roads on the loss costs was investigated. Surveyed data were analyzed using Excel and ArcGIS 10 software. The ordinary least square and the geographically weighted regression analyses were used to predict flood damage costs. Then, the estimates were delineated using voronoi geostatistical map tool. As a result, the factors account for the costs as high as 83%. The flood damage cost in a household varies between 24,315 through 37,693 Haitian Gourdes (approximately 607.875 through 942.325 U.S. Dollars). Severe damages were spotted in the urban area and in the rural section of Bassin whereas very low and low losses are essentially found in Labranle. The urban area was more severely affected by comparison with the rural area. Damages in the urban area are estimated at 41,206,869.57USD against 698,222,174.10 17,455,554.35USD in the rural area. In the urban part, damages were more severe in Raboteau-Jubilée and in Downtown but Bigot-Parc Vincent had the highest overall damage cost estimated at 9,729,368.95 USD. The lowest cost 7,602,040.42USD was recorded in Raboteau. Approximately, 39.38% of the rural area underwent very low to moderate damages. Bassin was the most severely struck by the 2004 floods, but Bayonnais turned out to have the highest loss cost: 4,988,487.66 USD. Bassin along with Labranle had the least damage cost, 2,956,131.11 and 2,268,321.41 USD respectively. Based on the findings, we recommended the implementation and diversification of income-generating activities, the maintenance and improvement of drains, sewers and gullies cleaning and the establishment of conservation practices upstream of the watersheds. In addition, the model should be applied and validated using actual official records as reference data. Finally, the use of a calculation-based approach is suggested to determine flood damage costs in order to reduce subjectivity during surveys.

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Savaux, Vincent. "Contribution to multipath channel estimation in an OFDM modulation context." Phd thesis, Supélec, 2013. http://tel.archives-ouvertes.fr/tel-00988283.

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In wireless communications systems, the transmission channel between the transmitter and the receiver antennas is one of the main sources of disruption for the signal. The multicarrier modulations, such as the orthogonal frequency division multiplexing (OFDM), are very robust against the multipath effect, and allow to recover the transmitted signal with a low error rate, when they are combined with a channel encoding. The channel estimation then plays a key role in the performance of the communications systems. In this PhD thesis, we study techniques based on least square (LS) and minimum mean square error (MMSE) estimators. The MMSE is optimal, but is much more complex than LS, and requires the a priori knowledge of the second order moment of the channel and the noise. In this presentation, two methods that allow to reach a performance close to the one of LMMSE while getting around its drawback are investigated. In another way, a third part of the presentation investigates the errors of estimation due to the interpolations.

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Tout, Bilal. "Identification of human-robot systems in physical interaction : application to muscle activity detection." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. https://ged.uphf.fr/nuxeo/site/esupversions/36d9eab3-c170-4e40-abb6-e6b4e27aeee2.

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Ces dernières années, l’interaction physique humain-robot est devenue un sujet de recherche important, par exemple pour des applications de rééducation. Cette thèse vise à améliorer ces interactions, dans le cadre du développement de contrôleurs basés modèles, par des approches d’identification paramétrique des modèles des systèmes en interaction. Le but est de développer des méthodes d’identification en tenant compte de la variabilité et de la complexité du corps humain, et en n'utilisant que les capteurs du système robotique pour éviter l'ajout de capteurs externes. Les différentes approches présentées dans cette thèse sont testées expérimentalement avec un système robotique à un degré de liberté (1-DDL) permettant d'interagir avec la main d'une personne.Après un 1er chapitre présentant l'état de l'art, le 2e chapitre aborde les méthodes d'identification développées en robotique ainsi que la problématique du filtrage, analysée en simulation et expérimentalement. La question du réglage du filtre passe-bas est adressée, et en particulier le choix de la fréquence de coupure qui reste délicate pour un système non-linéaire. Pour surmonter ces difficultés, une technique de filtrage utilisant un filtre de Kalman étendu (EKF) est développée à partir du modèle dynamique du robot. La formulation EKF proposée permet un réglage en fonction des propriétés connues du capteur et de la confiance dans l’estimation initiale des paramètres. Cette méthode est comparée en simulation puis expérimentalement avec différentes méthodes existantes en analysant la sensibilité à l’initialisation et au réglage du filtre. Les résultats montrent que la méthode proposée est prometteuse si l’EKF est correctement réglé.Le 3e chapitre porte sur l'identification en continu des paramètres du modèle dynamique d'un système passif en interaction avec un système robotique, en combinant des méthodes d’identification de la charge utile avec des algorithmes en ligne, sans capteurs externes. Ces méthodes sont validées en simulation et expérimentalement en utilisant le système à 1-DDL dont la poignée est attachée à des bandes élastiques pour imiter une articulation humaine passive. L’analyse de l’effet de l’ajustement des méthodes en ligne met en évidence qu’un compromis est nécessaire entre la vitesse de convergence et la précision des estimations des paramètres. Enfin, la comparaison des méthodes d’identification de la charge utile montre que les méthodes identifiant séparément les paramètres du système robotique et de l’humain passif donnent une meilleure précision et une plus faible complexité de calcul.Le 4e chapitre porte sur l'identification durant l'interaction humain-système robotique. Un modèle à raideur quadratique est proposé afin de mieux représenter le comportement de l’articulation humaine passive qu’un modèle linéaire. Par la suite, ce modèle est utilisé avec une méthode d’identification itérative basée sur le rejet d’outliers, pour détecter l’activité musculaire de l’humain sans capteurs externes. Cette méthode est comparée expérimentalement à une méthode non-itérative utilisant des signaux d’électromyographie (EMG), en adaptant le système à 1-DDL pour interagir avec le poignet et permettre d’évaluer l’activité des muscles fléchisseurs et extenseurs de deux sujets. La méthode itérative proposée sans signaux EMG donne des résultats proches de ceux obtenus avec la méthode utilisant les signaux EMG lorsqu’un modèle représentant bien le comportement de l’articulation humaine passive est choisi. Les résultats de détection de l’activité musculaire obtenus avec ces deux méthodes montrent un niveau de similarité satisfaisant avec ceux obtenus directement à partir des signaux EMG
Over the last years, physical human-robot interaction has become an important research subject, for example for rehabilitation applications. This PhD aims at improving these interactions, as part of model-based controllers development, using parametric identification approaches to identify models of the systems in interaction. The goal is to develop identification methods taking into account the variability and complexity of the human body, and only using the sensor of the robotic system to avoid adding external sensors. The different approaches presented in this thesis are tested experimentally on a one degree of freedom (1-DOF) system allowing the interaction with a person’s hand.After a 1st chapter presenting the state-of-the-art, the 2nd chapter tackles the identification methods developed in robotics as well as the issue of data filtering, analyzed both in simulation and experimentally. The question of the low-pass filter tuning is addressed, and in particular the choice of the cut-off frequency which remains delicate for a nonlinear system. To overcome these difficulties, a filtering technique using an extended Kalman filter (EKF) is developed from the robot dynamic model. The proposed EKF formulation allows a filter tuning depending on the known properties of the sensor and on the confidence on the initial parameters estimations. This method is compared in simulation and experimentally to different existing methods by analyzing its sensitivity to initialization and filter tuning. Results show that the proposed method is promising if the EKF is correctly tuned.The 3rd chapter concerns the continuous identification of the parameters of the model of a passive system interacting with a robotic system, by combining payload identification methods with online identification algorithms, without external sensors. These methods are validated in simulation and experimentally with the 1-DOF system whose handle is attached to elastic rubber bands to emulate a passive human joint. The analysis of the effects of the online methods tuning highlights a necessary trade-off between the convergence speed and the accuracy of the parameters estimates. Finally, the comparison of the payload identification methods shows that methods identifying separately the robotic system and the passive human parameters give better accuracy and a lower computation complexity.The 4th chapter deals with the identification during the human-robot interaction. A quadratic stiffness model is proposed to better fit the passive human joint behavior than a linear stiffness model. Then, this model is used with an iterative identification method based on outlier rejection technique, to detect the human user muscle activity without external sensors. This method is compared experimentally to a non-iterative method that uses electromyography (EMG), by adapting the 1-DOF system to interact with the wrist and to allow the detection of the flexor and extensor muscle activity of two human users. The proposed iterative identification method not using EMG signals achieves results close to those obtained with the non-iterative method using EMG signals when a model that correctly represents the passive human joint behavior is selected. The muscle activity detection results obtained with both methods show a satisfactory level of similarity compared to those obtained directly from EMG signals

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Chen, Jiaxiong. "Power System State Estimation Using Phasor Measurement Units." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/35.

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State estimation is widely used as a tool to evaluate the real time power system prevailing conditions. State estimation algorithms could suffer divergence under stressed system conditions. This dissertation first investigates impacts of variations of load levels and topology errors on the convergence property of the commonly used weighted least square (WLS) state estimator. The influence of topology errors on the condition number of the gain matrix in the state estimator is also analyzed. The minimum singular value of gain matrix is proposed to measure the distance between the operating point and state estimation divergence. To study the impact of the load increment on the convergence property of WLS state estimator, two types of load increment are utilized: one is the load increment of all load buses, and the other is a single load increment. In addition, phasor measurement unit (PMU) measurements are applied in state estimation to verify if they could solve the divergence problem and improve state estimation accuracy. The dissertation investigates the impacts of variations of line power flow increment and topology errors on convergence property of the WLS state estimator. A simple 3-bus system and the IEEE 118-bus system are used as the test cases to verify the common rule. Furthermore, the simulation results show that adding PMU measurements could generally improve the robustness of state estimation. Two new approaches for improving the robustness of the state estimation with PMU measurements are proposed. One is the equality-constrained state estimation with PMU measurements, and the other is Hachtel's matrix state estimation with PMU measurements approach. The dissertation also proposed a new heuristic approach for optimal placement of phasor measurement units (PMUs) in power system for improving state estimation accuracy. In the problem of adding PMU measurements into the estimator, two methods are investigated. Method I is to mix PMU measurements with conventional measurements in the estimator, and method II is to add PMU measurements through a post-processing step. These two methods can achieve very similar state estimation results, but method II is a more time-efficient approach which does not modify the existing state estimation software.

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Ångman, Josefin, and Pernilla Larsson. "Remittances and Development : Empirical evidence from 99 developing countries." Thesis, Uppsala universitet, Nationalekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-228416.

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Several studies have examined the effect of remittances on economic growth,poverty, education, and governance, among other factors, in developing countrieswith inconclusive results. Using annual panel data of 99 developing countries invarious empirical models, this study aim to answer the question how remittances affect a broader aspect of development using the Human Development Index asdependent variable. The findings indicate that there is a positive relationship between remittances and the level of human development in developing countries.

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Bai, Xiuqin. "Robust mixtures of regression models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18683.

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Doctor of Philosophy
Department of Statistics
Kun Chen and Weixin Yao
This proposal contains two projects that are related to robust mixture models. In the robust project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we demonstrate that the MLE, like the least squares estimate, is sensitive to outliers and heavy-tailed error distributions. We propose a robust estimation procedure and an EM-type algorithm to estimate the mixture regression models. Using a Monte Carlo simulation study, we demonstrate that the proposed new estimation method is robust and works much better than the MLE when there are outliers or the error distribution has heavy tails. In addition, the proposed robust method works comparably to the MLE when there are no outliers and the error is normal. In the second project, we propose a new robust mixture of linear mixed-effects models. The traditional mixture model with multiple linear mixed effects, assuming Gaussian distribution for random and error parts, is sensitive to outliers. We will propose a mixture of multiple linear mixed t-distributions to robustify the estimation procedure. An EM algorithm is provided to and the MLE under the assumption of t- distributions for error terms and random mixed effects. Furthermore, we propose to adaptively choose the degrees of freedom for the t-distribution using profile likelihood. In the simulation study, we demonstrate that our proposed model works comparably to the traditional estimation method when there are no outliers and the errors and random mixed effects are normally distributed, but works much better if there are outliers or the distributions of the errors and random mixed effects have heavy tails.

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Books on the topic "Least-Square estimator"

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1946-, Hsu Frank M., ed. Least square estimation with applications to digital signal processing. New York: Wiley, 1985.

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Cardot, Hervé, and Pascal Sarda. Functional Linear Regression. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.

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This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

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Book chapters on the topic "Least-Square estimator"

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Oh, Sang-Yeob, and Chan-Shik Ahn. "Moving Average Estimator Least Mean Square Using Echo Cancellation Algorithm." In IT Convergence and Security 2012, 319–24. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5860-5_38.

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Yu, Pan, Chen Xu, and Jinhua She. "Novel Least-Mean-Square-Based Adaptive Estimator for Unknown Periodic Disturbances." In Communications in Computer and Information Science, 399–411. Singapore: Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4753-8_30.

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Zhou, Si-Da, Ward Heylen, Paul Sas, and Li Liu. "Time-Frequency Domain Modal Parameter Estimation of Time-Varying Structures Using a Two-Step Least Square Estimator." In Topics in Modal Analysis I, Volume 5, 65–75. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2425-3_8.

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Awasthi, Neha, and Sukesha Sharma. "Comparative Analysis of Least Square, Minimum Mean Square Error and KALMAN Estimator Using DWT (Discrete Wavelet Transform)-Based MIMO-OFDM System." In Advances in Intelligent Systems and Computing, 233–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8618-3_25.

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Pan, Jian-Xin, and Kai-Tai Fang. "Generalized Least Square Estimation." In Growth Curve Models and Statistical Diagnostics, 38–76. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21812-0_2.

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Luigi, Ippoliti, and Romagnoli Luca. "Adjusted Least Square Estimation for Noisy Images." In Studies in Classification, Data Analysis, and Knowledge Organization, 255–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_21.

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Lin, Yachen, and Chung Chen. "Computation of Least Square Estimates Without Matrix Manipulation." In Data Mining and Knowledge Management, 81–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30537-8_9.

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Inan, Remzi, Mevlut Ersoy, and Cem Deniz Kumral. "Optimization of the Input/Output Linearization Feedback Controller with Simulated Annealing and Designing of a Novel Stator Flux-Based Model Reference Adaptive System Speed Estimator with Least Mean Square Adaptation Mechanism." In Trends in Data Engineering Methods for Intelligent Systems, 755–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79357-9_69.

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Mangalam, Vasudevan. "Least Square Estimation for Regression Parameters Under Lost Association." In Advances in Directional and Linear Statistics, 143–54. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2628-9_10.

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Wang, Jia, Haifeng Wang, Qingshan Liu, and Hanqing Lu. "Fast Global Motion Estimation Via Iterative Least-Square Method." In Computer Vision – ACCV 2006, 343–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11612704_35.

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Conference papers on the topic "Least-Square estimator"

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Gao, K., M. O. Ahmad, and M. N. S. Swamy. "A neural network least-square estimator." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137935.

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Zhang, Fu, Ehsan Keikha, Behrooz Shahsavari, and Roberto Horowitz. "Adaptive Mismatch Compensation for Rate Integrating Vibratory Gyroscopes With Improved Convergence Rate." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6053.

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This paper presents an online adaptive algorithm to compensate damping and stiffness frequency mismatches in rate integrating Coriolis Vibratory Gyroscopes (CVGs). The proposed adaptive compensator consists of a least square estimator that estimates the damping and frequency mismatches, and an online compensator that corrects the mismatches. In order to improve the adaptive compensator’s convergence rate, we introduce a calibration phase where we identify relations between the unknown parameters (i.e. mismatches, rotation rate and rotation angle). Calibration results show that the unknown parameters lie on a hyperplane. When the gyro is in operation, we project parameters estimated from the least square estimator onto the hyperplane. The projection will reduce the degrees of freedom in parameter estimates, thus guaranteeing persistence of excitation and improving convergence rate. Simulation results show that utilization of the projection method will drastically improve convergence rate of the least square estimator and improve gyro performance.

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Htoon, Zaw Lay, Shahrul Na'im Sidek, Sado Fatai, and Muhammad Mahbubur Rashid. "Estimation of Upper Limb Impedance Parameters Using Recursive Least Square Estimator." In 2016 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2016. http://dx.doi.org/10.1109/iccce.2016.41.

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Coumou, David J. "Sub pixel accuracy of fiducial marks using least square estimator." In Optics & Photonics 2005, edited by Oliver E. Drummond. SPIE, 2005. http://dx.doi.org/10.1117/12.617267.

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Oh, Sang-Yeob, and Kyung-Yong Chung. "Robust Vocabulary Recognition Model Using Average Estimator Least Mean Square Filter." In 2013 International Conference on Information Science and Applications (ICISA). IEEE, 2013. http://dx.doi.org/10.1109/icisa.2013.6579393.

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Ferraz, R. G., L. U. Iurinic, A. D. Filomena, and A. S. Bretas. "High impedance fault location formulation: a least square estimator based approach." In 12th IET International Conference on Developments in Power System Protection (DPSP 2014). Institution of Engineering and Technology, 2014. http://dx.doi.org/10.1049/cp.2014.0046.

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Brady, Michael R., and Pavlos P. Vlachos. "Novel, Subpixel Resolution Schemes for Particle Image Velocimeters." In ASME 2004 Heat Transfer/Fluids Engineering Summer Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/ht-fed2004-56838.

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Simultaneous velocity and size measurements are of great importance for resolving the dynamics of polydispersed multiphase flows. This effort explores novel sub-resolution particle center estimation algorithms for Digital Particle Image Velocimetry (DPIV) and Particle Tracking Velocimetry (DPTV). In addition these schemes provide direct measurements of the apparent particle image diameter using the same DPIV/DPTV recordings. Three novel fitting schemes were developed and compared with established methods such as center of mass, three-point Gaussian fit estimator and a least-square fit method. The new methods consist of two variations of a four point gaussian estimator and a variation of the conventional least-squares fit estimator. The new methods eliminate the bias error due to pixel discretization, thus significantly reducing the total error in the position and sizing measurement compared to the classic three point and least squares Gaussian estimators. In addition, the accuracy of the least-squares fits were essentially independent of the true particle diameter and significantly reduced the particle position error compared with current estimation schemes.

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Wang, Yongxue. "Adaptive Channel Estimator Based on Least Mean Square Error for Wireless LAN." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.929.

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Ando, Kengo, Hiroki Iimori, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David Gonzalez G, and Osvaldo Gonsa. "An Iterative Discrete Least Square Estimator with Dynamic Parameterization via Deep-Unfolding." In 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022. http://dx.doi.org/10.1109/ieeeconf56349.2022.10051860.

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Alhasant, A. I., B. S. Sharif, C. C. Tsimenidis, and J. A. Neasham. "Low complexity least-square estimator for RSS-based localization in Wireless Sensor Networks." In 2012 International Conference on Communications and Information Technology (ICCIT). IEEE, 2012. http://dx.doi.org/10.1109/iccitechnol.2012.6285818.

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Reports on the topic "Least-Square estimator"

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Hall, P., and J. S. Marron. Extent to which Least-Squares Cross-Validation Minimises Integrated Square Error in Nonparametric Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada153789.

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Alonso Sanabria, Juan David, Luis Fernando Melo-Velandia, and Daniel Parra-Amado. Unveiling the critical role of forest areas amidst climate change: The Latin American case. Banco de la República, October 2023. http://dx.doi.org/10.32468/be.1254.

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Although greenhouse gas emissions from the Latin America (LAC) region are not particularly significant, climate change is a worldwide challenge. Hence, we analyze the main factors that increase and mitigate emissions in LAC countries by emphasising the importance of preserving and safeguarding forested regions. To do that, we estimate a Panel Fully Modified Ordinary Least Square model for Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, and Peru by using a sample period between 1970 and 2018. We find that an increase of 1% in forested area leads to a reduction of CO2 (Kt per capita) emissions by 0.23%. From the policy perspective, our findings draw attention towards the promotion of reforestation and afforestation initiatives. Furthermore, these long term policies will hold substantial significance, given the region's immense potential, with more than a fifth of the world's forest reserves.

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Bedoya-Maya, Felipe, Agustina Calatayud, and Vileydy Gonzalez-Mejia. Estimating the effect of urban road congestion on air quality in Latin America. Inter-American Development Bank, October 2022. http://dx.doi.org/10.18235/0004512.

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Road congestion and air pollution are key challenges for quality of life in urban settings. This research leverages highly disaggregated crowdsourced data from Latin America to study the effect of road congestion on levels of carbon monoxide, nitrogen dioxide, and particulate matter in four of the most congested cities in developing countries: Bogota, Buenos Aires, Mexico City, and Santiago. Based on a panel data econometric approach with over 4.4 billion records from Waze and hourly data from 54 air monitoring stations for 2019, our two-stage least square model shows a cumulative increase of 0.6% in response to a 1% of road congestion on the three air pollutants. Moreover, we find a nonlinear relationship between road congestion and air quality and estimate the threshold above which the effect decays. This study provides evidence that supports public policies designed to make urban mobility more sustainable by implementing measures to reduce road congestion in developing contexts.

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Neves, Mateus C. R., Felipe De Figueiredo Silva, and Carlos Otávio Freitas. The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003404.

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In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

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Apeti, Ablam Estel, and Eyah Denise Edoh. Finding the Missing Stone: Mobile Money and the Quality of Tax Policy and Administration. Institute of Development Studies, January 2024. http://dx.doi.org/10.19088/ictd.2024.006.

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Making tax administration more efficient and maximising voluntary compliance is a very difficult task for developing countries. In this paper, we analyse the effect of mobile money payments on the quality of tax policy and administration for a large sample of countries in developing economies. We use the World Bank indicator on efficiency of revenue mobilisation as a measure of the quality of tax policy and administration and employ an entropy balancing method to show that mobile money payments improve the quality of tax systems. This result is robust to several robustness tests, including sample alteration, alternative measures of mobile money, controlling for other aspects of tax policy, and alternative estimation methods such as GMM-system, event study approach and ordinary least square. In addition, our results show that the positive effect of mobile money on tax systems depends on the level of development, financial development, the state’s legitimacy, a country’s fiscal space, the number of available products/companies, the type of mobile money services, and the geographic position of countries. Finally, we highlight some potential mechanisms underlying these findings through lower tax compliance burden, smaller informal sector, and lower corruption.

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