Relevant bibliographies by topics / Estimation theory

Academic literature on the topic 'Estimation theory'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Estimation theory"

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Odoh, Dr Mcchester, and Dr Ihedigbo Chinedum E. "Estimation Theory." IOSR Journal of Computer Engineering 16, no. 6 (2014): 30–35. http://dx.doi.org/10.9790/0661-16623035.

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Saikkonen, Pentti. "Asymptotically Efficient Estimation of Cointegration Regressions." Econometric Theory 7, no. 1 (March 1991): 1–21. http://dx.doi.org/10.1017/s0266466600004217.

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An asymptotic optimality theory for the estimation of cointegration regressions is developed in this paper. The theory applies to a reasonably wide class of estimators without making any specific assumptions about the probability distribution or short-run dynamics of the data-generating process. Due to the nonstandard nature of the estimation problem, the conventional minimum variance criterion does not provide a convenient measure of asymptotic efficiency. An alternative criterion, based on the concentration or peakedness of the limiting distribution of an estimator, is therefore adopted. The limiting distribution of estimators with maximum asymptotic efficiency is characterized in the paper and used to discuss the optimality of some known estimators. A new asymptotically efficient estimator is also introduced. This estimator is obtained from the ordinary least-squares estimator by a time domain correction which is nonparametric in the sense that no assumption of a finite parameter model is required. The estimator can be computed with least squares without any initial estimations.
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Altounji, Nizar, Mohamed Bisher Zeina, and Moustafa Mazhar Ranneh. "Introduction to Neutrosophic Bayes Estimation Theory." Galoitica: Journal of Mathematical Structures and Applications 7, no. 1 (2023): 43–50. http://dx.doi.org/10.54216/gjmsa.070105.

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This research presents the concept of neutrosophic Bayesian estimation defining the neutrosophic loss function, neutrosophic risk function, neutrosophic posterior risk function and neutrosophic maximum a posteriori estimator. Minimization of the neutrosophic posterior risk of the estimator is also discussed. An algebraic isomorphism is used to simplify equations solving. As an application of the presented theorems, a sample drawn from a neutrosophic gamma distribution with a conjugate prior is discussed and studied and the parameter of the formulated distribution is successfully estimated using neutrosophic quadratic loss function which results an estimator that equals the posterior mean.
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Rodríguez-García, Marco A., Isaac Pérez Castillo, and P. Barberis-Blostein. "Efficient qubit phase estimation using adaptive measurements." Quantum 5 (June 4, 2021): 467. http://dx.doi.org/10.22331/q-2021-06-04-467.

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Estimating correctly the quantum phase of a physical system is a central problem in quantum parameter estimation theory due to its wide range of applications from quantum metrology to cryptography. Ideally, the optimal quantum estimator is given by the so-called quantum Cramér-Rao bound, so any measurement strategy aims to obtain estimations as close as possible to it. However, more often than not, the current state-of-the-art methods to estimate quantum phases fail to reach this bound as they rely on maximum likelihood estimators of non-identifiable likelihood functions. In this work we thoroughly review various schemes for estimating the phase of a qubit, identifying the underlying problem which prohibits these methods to reach the quantum Cramér-Rao bound, and propose a new adaptive scheme based on covariant measurements to circumvent this problem. Our findings are carefully checked by Monte Carlo simulations, showing that the method we propose is both mathematically and experimentally more realistic and more efficient than the methods currently available.
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Ghlib, Imane, Youcef Messlem, and Zakaria Chedjara. "An Improved Sensorless Control of Induction Motor Using ADALINE: Theory and Experiment." Journal Européen des Systèmes Automatisés​ 55, no. 2 (April 30, 2022): 221–27. http://dx.doi.org/10.18280/jesa.550209.

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This article develops a new observer to achieve highly speed estimation for induction motor control. Its principle consists of estimating flux and speed separately, by using linear Luenberger as flux observer and intelligent PI controller as rotor speed estimator. Considering that is generally challenging to established proper PI parameters, the ADALINE algorithm is integrated as a trainable module to automatically calculate the KP and the KI gains in each iteration during all operation induction motor. This algorithm helps to search on the one hand the best regulation of the controller in real-time, taking into account the objectives to achieve (an accurate estimation) and the respect constraints (heavy calculation). On the other hand, it makes it possible to improve the adaptive Luenberger estimation problem in the unobservable zone (low speed). The experimental comparison of the observers demonstrates the visible improvement of the enhanced observer. It can converge to real speed in a short time with less static and transient error.
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Xu, Ke-Li. "REWEIGHTED FUNCTIONAL ESTIMATION OF DIFFUSION MODELS." Econometric Theory 26, no. 2 (September 30, 2009): 541–63. http://dx.doi.org/10.1017/s0266466609100087.

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The local linear method is popular in estimating nonparametric continuous-time diffusion models, but it may produce negative results for the diffusion (or volatility) functions and thus lead to insensible inference. We demonstrate this using U.S. interest rate data. We propose a new functional estimation method of the diffusion coefficient based on reweighting the conventional Nadaraya–Watson estimator. It preserves the appealing bias properties of the local linear estimator and is guaranteed to be nonnegative in finite samples. A limit theory is developed under mild requirements (recurrence) of the data generating mechanism without assuming stationarity or ergodicity.
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Chan, Hock Peng, Chiang-Wee Heng, and Ajay Jasra. "Theory of segmented particle filters." Advances in Applied Probability 48, no. 1 (March 2016): 69–87. http://dx.doi.org/10.1017/apr.2015.7.

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AbstractWe study the asymptotic behavior of a new particle filter approach for the estimation of hidden Markov models. In particular, we develop an algorithm where the latent-state sequence is segmented into multiple shorter portions, with an estimation technique based upon a separate particle filter in each portion. The partitioning facilitates the use of parallel processing, which reduces the wall-clock computational time. Based upon this approach, we introduce new estimators of the latent states and likelihood which have similar or better variance properties compared to estimators derived from standard particle filters. We show that the likelihood function estimator is unbiased, and show asymptotic normality of the underlying estimators.
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Zieliński, Ryszard. "Theory of parameter estimation." Banach Center Publications 41, no. 2 (1997): 209–20. http://dx.doi.org/10.4064/-41-2-209-220.

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Ziegel, Eric R., E. L. Lehmann, and George Casella. "Theory of Point Estimation." Technometrics 41, no. 3 (August 1999): 274. http://dx.doi.org/10.2307/1270597.

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Strawderman, William E., E. L. Lehmann, and George Casella. "Theory of Point Estimation." Journal of the American Statistical Association 95, no. 449 (March 2000): 329. http://dx.doi.org/10.2307/2669560.

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Dissertations / Theses on the topic "Estimation theory"

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Baur, Cordula. "Risk Estimation in Portfolio Theory." St. Gallen, 2007. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/05609706001/$FILE/05609706001.pdf.

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Feinstein, Jonathan S. "Detection controlled estimation : theory and applications." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14868.

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Qin, Li. "Nonparametric estimation in actuarial ruin theory." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608513.

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Khorramzadeh, Yasamin. "Network Reliability: Theory, Estimation, and Applications." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64383.

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Network reliability is the probabilistic measure that determines whether a network remains functional when its elements fail at random. Definition of functionality varies depending on the problem of interest, thus network reliability has much potential as a unifying framework to study a broad range of problems arising in complex network contexts. However, since its introduction in the 1950's, network reliability has remained more of an interesting theoretical construct than a practical tool. In large part, this is due to well-established complexity costs for both its evaluation and approximation, which has led to the classification of network reliability as a NP-Hard problem. In this dissertation we present an algorithm to estimate network reliability and then utilize it to evaluate the reliability of large networks under various descriptions of functionality. The primary goal of this dissertation is to pose network reliability as a general scheme that provides a practical and efficiently computable observable to distinguish different networks. Employing this concept, we are able to demonstrate how local structural changes can impose global consequences. We further use network reliability to assess the most critical network entities which ensure a network's reliability. We investigate each of these aspects of reliability by demonstrating some example applications.
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Yang, Qian. "Stock bubbles : The theory and estimation." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/3597.

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This work attempts to make a breakthrough in the empirical research of market inefficiency by introducing a new approach, the value frontier method, to estimate the magnitude of stock bubbles, which has been an interesting topic that has attracted a lot of research attention in the past. The theoretical framework stems from the basic argument of Blanchard & Watson’s (1982) rational expectation of asset value that should be equal to the fundamental value of the stock, and the argument of Scheinkman & Xiong (2003) and Hong, Scheinkman & Xiong (2006) that bubbles are formed by heterogeneous beliefs which can be refined as the optimism effect and the resale option effect. The applications of the value frontier methodology are demonstrated in this work at the market level and the firm level respectively. The estimated bubbles at the market level enable us to analyse bubble changes over time among 37 countries across the world, which helps further examine the relationship between economic factors (e.g. inflation) and bubbles. Firm-level bubbles are estimated in two developed markets, the US and the UK, as well as one emerging market, China. We found that the market-average bubble is less volatile than industry-level bubbles. This finding provides a compelling explanation to the failure of many existing studies in testing the existence of bubbles at the whole market level. In addition, the significant decreasing trend of Chinese bubbles and their co-moving tendency with the UK and the US markets offer us evidence in support of our argument that even in an immature market, investors can improve their investment perceptions towards rationality by learning not only from previous experience but also from other opened markets. Furthermore, following the arguments of “sustainable bubbles” from Binswanger (1999) and Scheinkman & Xiong (2003), we reinforce their claims at the end that a market with bubbles can also be labelled efficient; in particular, it has three forms of efficiency. First, a market without bubbles is completely efficient from the perspective of investors’ responsiveness to given information; secondly, a market with “sustainable bubbles” (bubbles that co-move with the economy), which results from rational responses to economic conditions, is in the strong form of information-responsive efficiency; thirdly, a market with “non-sustainable bubbles”, i.e. the bubble changes are not linked closely with economic foundations, is in the weak form of information-responsive efficiency.
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Whaley, Dewey Lonzo. "The Interquartile Range: Theory and Estimation." Digital Commons @ East Tennessee State University, 2005. https://dc.etsu.edu/etd/1030.

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The interquartile range (IQR) is used to describe the spread of a distribution. In an introductory statistics course, the IQR might be introduced as simply the “range within which the middle half of the data points lie.” In other words, it is the distance between the two quartiles, IQR = Q3 - Q1. We will compute the population IQR, the expected value, and the variance of the sample IQR for various continuous distributions. In addition, a bootstrap confidence interval for the population IQR will be evaluated.
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Pilota, Evdoxia. "Extreme value thepory forvalue at risk estimation : Theory and empirical application." Thesis, University of Essex, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499763.

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Al-Baidhani, Fadil Ajab. "Reliability theory in operational research." Thesis, University of St Andrews, 1991. http://hdl.handle.net/10023/13745.

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This thesis is concerned principally with the problem of estimating the parameters of the Weibull and Beta distributions using several different techniques. These distributions are used in the area of reliability testing and it is important to achieve the best estimates possible of the parameters involved. After considering several accepted methods of estimating the relevant parameters, it is considered that the best method depends on the aim of the analysis, and on the value of the shape parameter β. For estimating the two-parameter Weibull distribution, it is recommended that Generalized Least Squares (GLS) is the best method to use for values of β between 0.5 and 30. However, Maximum Likelihood Estimator (MLE) is a good method for estimating quantiles. On this basis, the three-parameter Weibull distribution is investigated. The traditional parametrization is compared with a new parametrization developed in this work. By considering parameter effects and intrinsic curvature it is shown that the new parametrization results in a linear effect of the shape parameter. Also it has advantages in quantile estimation because of its ability to provide estimates for a wider range of data sets. A less frequently used distribution in the field of reliability is the Beta distribution. The lack of frequency of its use is partly due to the difficulty in estimating its parameters. A simple, applicable method is developed here of estimating these parameters. This 'group method' involves estimating the two ends of the distribution. It is shown that this procedure can be used, together with other methods of estimating the two- parameter Beta distribution successfully to estimate the four-parameter Beta distribution.
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Vishwanath, T. G. "Adaptive estimation theory with real-time implementation." Thesis, University of the West of Scotland, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376566.

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Yang, Ming. "When Decision Meets Estimation: Theory and Applications." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/627.

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In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms.
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Books on the topic "Estimation theory"

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Lehmann, E. L. Theory of point estimation. New York: Springer, 1997.

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Lehmann, E. L. Theory of Point Estimation. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-8071-3.

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Kubáček, Lubomír. Foundations of estimation theory. Amsterdam: Elsevier, 1988.

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Lehmann, E. L. Theory of point estimation. 2nd ed. New York: Springer, 1998.

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Ross, Gavin J. S. Nonlinear estimation. New York: Springer-Verlag, 1990.

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Trees, Harry L. Van. Detection, estimation, and modulation theory. Hoboken, N.J: Wiley, 2003.

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Akahira, Masafumi. Asymptotic theory of statistical estimation. Ibaraki, Japan: Institute of Mathematics, University of Tsukuba, 1991.

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Dowdy, Penny. Estimation. New York: Crabtree Pub., 2008.

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Wegkamp, M. H. Entropy methods in statistical estimation. Amsterdam, The Netherlands: CWI, 1998.

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Maine, Richard E. Identification of dynamic systems: Theory and formulation. Washington, D.C: National Aeronautics and Space Administration, Scientific and Technical Information Branch, 1985.

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Book chapters on the topic "Estimation theory"

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Wüthrich, Mario V., and Michael Merz. "Estimation Theory." In Springer Actuarial, 49–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12409-9_3.

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AbstractThis chapter is on classical statistical decision theory. It is an important chapter for historical reasons, but it also provides the right mathematical grounding and intuition for more modern statistical tools from data science and machine learning. In particular, we discuss maximum likelihood estimation (MLE), unbiasedness, consistency and asymptotic normality of MLEs in this chapter.
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Bensoussan, Alain. "Estimation Theory." In Interdisciplinary Applied Mathematics, 45–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75456-7_4.

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Abramovich, Felix, and Ya’acov Ritov. "*Nonparametric Estimation." In Statistical Theory, 157–84. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175407-9.

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Abramovich, Felix, and Ya’acov Ritov. "Point Estimation." In Statistical Theory, 15–36. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003175407-2.

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Watanabe, Yu. "Classical Estimation Theory." In Formulation of Uncertainty Relation Between Error and Disturbance in Quantum Measurement by Using Quantum Estimation Theory, 19–36. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54493-7_3.

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Watanabe, Yu. "Quantum Estimation Theory." In Formulation of Uncertainty Relation Between Error and Disturbance in Quantum Measurement by Using Quantum Estimation Theory, 37–44. Tokyo: Springer Japan, 2013. http://dx.doi.org/10.1007/978-4-431-54493-7_4.

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Levy, Bernard C. "Parameter Estimation Theory." In Principles of Signal Detection and Parameter Estimation, 1–56. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76544-0_4.

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Härdle, Wolfgang Karl, and Zdeněk Hlávka. "Theory of Estimation." In Multivariate Statistics, 89–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36005-3_6.

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Pham, Hoang. "Theory of Estimation." In Springer Series in Reliability Engineering, 77–120. London: Springer London, 2006. http://dx.doi.org/10.1007/1-84628-295-0_3.

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Wang, Dayi, Maodeng Li, Xiangyu Huang, and Xiaowen Zhang. "Point Estimation Theory." In Space Science and Technologies, 23–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4879-6_2.

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Conference papers on the topic "Estimation theory"

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Gustafson, Erik, Henry Lamm, and Judah Unmuth-Yockey. "Quantum mean estimation for lattice field theory." In Quantum mean estimation for lattice field theory. US DOE, 2023. http://dx.doi.org/10.2172/1983816.

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"12 - Estimation Theory." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628652.

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Baygun, B., and A. O. Hero. "An order selection criterion via simultaneous estimation/detection theory." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205568.

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Smith, A., A. Monti, and F. Ponci. "Confidence interval estimation using polynomial chaos theory." In 2008 IEEE Iinternational Workshop on Advanced Methods for Uncertainty Estimation in Measurement (AMUEM). IEEE, 2008. http://dx.doi.org/10.1109/amuem.2008.4589927.

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Malkin, R. A., and T. C. Pilkington. "Defibrillation efficacy estimation using Bayesian estimation theory." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319059.

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Bashash, Saeid, and Hosam K. Fathy. "Battery State of Health and Charge Estimation Using Polynomial Chaos Theory." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-4088.

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In this effort, we use the generalized Polynomial Chaos theory (gPC) for the real-time state and parameter estimation of electrochemical batteries. We use an equivalent circuit battery model, comprising two states and five parameters, and formulate the online parameter estimation problem using battery current and voltage measurements. Using a combination of the conventional recursive gradient-based search algorithm and gPC framework, we propose a novel battery parameter estimation strategy capable of estimating both battery state-of-charge (SOC) and parameters related to battery health, e.g., battery charge capacity, internal resistance, and relaxation time constant. Using a combination of experimental tests and numerical simulations, we examine and demonstrate the effectiveness of the proposed battery estimation method.
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Prabhu, Vinay Uday, and Dimitris Toumpakaris. "An MSE-tunable linear estimator with conditional dominance over least-squares estimation." In 2009 IEEE Information Theory Workshop on Networking and Information Theory (ITW). IEEE, 2009. http://dx.doi.org/10.1109/itwnit.2009.5158544.

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Ghanem, Samah A. M. "Network coding: Connections between information theory and estimation theory." In 2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 2016. http://dx.doi.org/10.1109/wowmom.2016.7523577.

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Zia, Amin, James P. Reilly, and Shahram Shirani. "Distributed Estimation; Three Theorems." In 2007 IEEE Information Theory Workshop. IEEE, 2007. http://dx.doi.org/10.1109/itw.2007.4313128.

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Witt, Carsten. "Theory of estimation-of-distribution algorithms." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3323367.

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Reports on the topic "Estimation theory"

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Chow, Winston C. Estimation Theory with Fractional Gaussian Noise. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301443.

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Kailath, Thomas. Studies in Estimation Theory, Applications and Implementations. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada210712.

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Kailath, Thomas. Studies in Estimation Theory, Applications and Implementation. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256964.

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Helpman, Elhanan, Oleg Itskhoki, Marc-Andreas Muendler, and Stephen Redding. Trade and Inequality: From Theory to Estimation. Cambridge, MA: National Bureau of Economic Research, April 2012. http://dx.doi.org/10.3386/w17991.

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Libura, Marek. Multidimensional Item Response Theory Estimation: A computer program. Fort Belvoir, VA: Defense Technical Information Center, October 1987. http://dx.doi.org/10.21236/ada197160.

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Brockett, R. W. Modeling and Estimation Theory for Stochastic Dynamical Systems. Fort Belvoir, VA: Defense Technical Information Center, September 1986. http://dx.doi.org/10.21236/ada172902.

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Beaudry, Paul, David Green, and Benjamin Sand. Spatial Equilibrium with Unemployment and Wage Bargaining: Theory and Estimation. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19118.

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Simpson, Douglas G., Raymond J. Carroll, and David Ruppert. M-Estimation for Discrete Data. Asymptotic Distribution Theory and Implications. Fort Belvoir, VA: Defense Technical Information Center, October 1985. http://dx.doi.org/10.21236/ada162779.

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Simpson, Douglas G., Raymond J. Carroll, and David Ruppert. M-Estimation for Discrete Data: Asymptotic Distribution Theory and Implications. Fort Belvoir, VA: Defense Technical Information Center, November 1985. http://dx.doi.org/10.21236/ada168532.

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Fukumoto, Koji, and Haruyuki Konishi. Estimation for Thermal Deformation of the Panel by Effective Width Theory. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0346.

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