Relevant bibliographies by topics / Mathematics Estimation theory

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

Academic literature on the topic 'Mathematics Estimation theory'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Mathematics Estimation theory"

1

Girko, Vyacheslav L. "Spectral theory of minimax estimation." Acta Applicandae Mathematicae 43, no. 1 (April 1996): 59–69. http://dx.doi.org/10.1007/bf00046987.

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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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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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Yang, Da. "Interval Estimation and Hypothesis Testing." Applied Mechanics and Materials 543-547 (March 2014): 1717–20. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1717.

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Mathematical statistics is a branch of mathematics has extensive application of interval estimation and hypothesis testing, which are two important problems of statistical inference. As two important statistical inference methods, interval estimation and hypothesis testing problem is more and more widely used in the field of economic management, finance and insurance, scientific research, engineering technology, the science of decision functions are recognized by more and more people. Can go further to establish mutual influence and communication between the interval estimation and hypothesis testing, can use the theory to explain the problem of interval estimation of parameter hypothesis test, this is an important problem to improve the statistical inference theory. Therefore, the basis on the internal relations between the interval estimation and hypothesis test for deep research, explain the problem of hypothesis testing and interval estimation from the point of view, discusses the difference and connection between the two.
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Rovigatti, Gabriele, and Vincenzo Mollisi. "Theory and Practice of Total-Factor Productivity Estimation: The Control Function Approach using Stata." Stata Journal: Promoting communications on statistics and Stata 18, no. 3 (September 2018): 618–62. http://dx.doi.org/10.1177/1536867x1801800307.

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Alongside instrumental-variables and fixed-effects approaches, the control function approach is the most widely used in production function estimation. Olley and Pakes (1996, Econometrica 64: 1263–1297), Levinsohn and Petrin (2003, Review of Economic Studies 70: 317–341), and Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451) have all contributed to the field by proposing two-step estimation procedures, whereas Wooldridge (2009, Economics Letters 104: 112–114) showed how to perform a consistent estimation within a single-step generalized method of moments framework. In this article, we propose a new estimator based on Wooldridge's estimation procedure, using dynamic panel instruments à la Blundell and Bond (1998, Journal of Econometrics 87: 115–143), and we evaluate its performance by using Monte Carlo simulations. We also present the new command prodest for production function estimation, and we show its main features and strengths in a comparative analysis with other community-contributed commands. Finally, we provide evidence of the numerical challenges faced when using the Olley–Pakes and Levinsohn–Petrin estimators with the Ackerberg–Caves–Frazer correction in empirical applications, and we document how the generalized method of moments estimates vary depending on the optimizer or starting points used.
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Narens, Louis. "A Theory of Ratio Magnitude Estimation." Journal of Mathematical Psychology 40, no. 2 (June 1996): 109–29. http://dx.doi.org/10.1006/jmps.1996.0011.

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Penskaya, M. Ya. "Application of regularizing operators in estimation theory." Journal of Soviet Mathematics 53, no. 6 (March 1991): 655–60. http://dx.doi.org/10.1007/bf01095377.

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Wang, Alan P. "Scattering theory and linear state-space estimation." Journal of Mathematical Analysis and Applications 120, no. 2 (December 1986): 397–415. http://dx.doi.org/10.1016/0022-247x(86)90165-4.

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Yang, L. X., D. N. Hào, and H. Sahli. "Motion estimation by hybrid diffusion: theory and implementation." Journal of Inverse and Ill-posed Problems 14, no. 3 (May 2006): 307–30. http://dx.doi.org/10.1515/156939406777340900.

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Wechsler, H., Z. Duric, F. Li, and V. Cherkassky. "Motion estimation using statistical learning theory." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 4 (April 2004): 466–78. http://dx.doi.org/10.1109/tpami.2004.1265862.

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Dissertations / Theses on the topic "Mathematics Estimation theory"

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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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Hemvanich, Sanha. "GMM estimation for nonignorable missing data : theory and practice." Thesis, University of Warwick, 2007. http://wrap.warwick.ac.uk/1148/.

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This thesis develops several Generalized Method ofMoments (GMM) estimators for analysing Not Missing at Random (NMAR) data, which is commonly referred to as the self-selection problem in an economic context. We extend the semiparametric estimation procedures of Ramalho and Smith (2003) to include the case where the missing data mechanism (MDM) depends on both a continuous response variable and covariates. Within this framework, it is possible to avoid imposing any assumptions on the missing data mechanism. We also discuss the asymptotic properties of the proposed GMM estimators and establish the connections of them to the GMM estimators of Ramalho and Smith and to the pseudolikelihood estimators of Tang, Little and Raghunathan (2003). All of the aforementioned estimators are then compared to other standard estimators for missing data such as the inverse probability weighted and sample selection model estimators in a number of Monte Carlo experiments. As an empirical application, these estimators are also applied to analyse the UK wage distribution. We found that, in many circumstances, our proposed estimators perform better than the other estimators described; especially when there is no exclusion restriction or other additional information available. Finally, we summarise that, since the true MDM is unlikely to be known, several estimators which impose different assumptions on the MDM should be used together to examine the sensitivity of the outcomes of interest with respect to the assumptions made and the estimation procedures adopted.
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Gaby, James Eliot. "Artificial intelligence applied to spectrum estimation." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15715.

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Yoo, Hyungsuk. "Quality of the Volterra transfer function estimation /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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Xu, (Bill) Ke. "Efficient parameterization and estimation of spatio-temporal dynamic models /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3137766.

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Mupambirei, Rodwel. "Dynamic and robust estimation of risk and return in modern portfolio theory." Master's thesis, University of Cape Town, 2008. http://hdl.handle.net/11427/4913.

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Includes bibliographical references (leaves 134-138).
The portfolio selection method developed by Markowitz gives a rational investor a way of evaluating different investment options in a portfolio using the expected return and variance of the returns. Sharpe uses the same optimization approach but estimates the mean and covariance in a regression framework using the index models. Sharpe makes a crucial assumption that the residuals from different assets are uncorrelated and that the beta estimates are constant. When the Sharpe model parameters are estimated using ordinary least squares, the regression assumptions are violated when there is significant autocorrelation and heteroskedasticity in the residuals. Furthermore, the presence of outlying observations in the data leads to unreliable estimates when the ordinary least squares method is used. We find significant correlation in the residuals from different shares and thus we use the Troskie-Hossain model which relaxes this assumption and ultimately produces an efficient frontier that is almost identical to the Markowitz model. The combination of the GARCH and AR models to remove both autocorrelation and heteroskedasticity is used on the single index model and it causes the efficient frontier to shift significantly to the left. Using dynamic estimation through the Kalman filter, it is noticed that the beta coefficients are not constant and that the resulting efficient frontiers significantly outperform the Sharpe model. In order to deal with the problem of outlying observations in the data, we propose using the Minimum Covariance Determinant, (MCD) estimator as a robust version of the Markowitz formulation. Robust alternatives to the ordinary lea.st squares estimator are also investigated and they all cause the efficient frontier to shift to the left. Finally, to solve the problem of collinearity in the multiple index framework, we construct orthogonal indices using principal components regression to estimate the efficient frontier.
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Young, Po-yuk. "Profile of good computational estimators related mathematical variables and common strategies used." [Hong Kong] : University of Hong Kong, 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14420478.

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Bellaire, Robert Louis. "Nonlinear estimation with applications to target tracking." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15399.

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Davis, J. Wade. "Wavelet-based methods for estimation and discrimination /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3099616.

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Song, Changyong. "Function estimation via wavelets in the presence of interval censoring /." free to MU campus, to others for purchase, 1998. http://wwwlib.umi.com/cr/mo/fullcit?p9924927.

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Books on the topic "Mathematics Estimation theory"

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Kay, Steven M. Modern spectral estimation: Theory and application. Englewood Cliffs, N.J: Prentice Hall, 1988.

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Gradient estimation via perturbation analysis. Boston: Kluwer Academic Publishers, 1991.

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Bock, Hans Georg. Model Based Parameter Estimation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Harney, Hanns L. Bayesian Inference: Parameter Estimation and Decisions. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.

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Händel, Peter. Estimation methods for narrow-band signals. Uppsala: Uppsala University, Dept. of Technology Systems and Control Group, 1993.

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Nacházel, Karel. Teorie odhadu v hydrologii a ve vodním hospodářství. Praha: Academia, 1986.

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1943-, Woods John W., ed. Probability, random processes, and estimation theory for engineers. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Stark, Henry. Probability, random processes, and estimation theory for engineers. 2nd ed. Englewood Cliffs, N.J: Prentice Hall, 1994.

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service), SpringerLink (Online, ed. Mono- and Multivariable Control and Estimation: Linear, Quadratic and LMI Methods. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Tree structured function estimation with Haar wavelets. Hamburg: Verlag Dr. Kovač, 1999.

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Book chapters on the topic "Mathematics Estimation theory"

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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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Asiri, Sharefa, and Taous-Meriem Laleg-Kirati. "Wave Velocity Estimation in Heterogeneous Media." In Intelligent Mathematics II: Applied Mathematics and Approximation Theory, 303–12. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30322-2_21.

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An, Xinming, and Yiu-Fai Yung. "Notes on the Estimation of Item Response Theory Models." In Springer Proceedings in Mathematics & Statistics, 305–17. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9348-8_19.

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Bhardwaj, Neha, and Naokant Deo. "A Better Error Estimation On Szász–Baskakov–Durrmeyer Operators." In Advances in Applied Mathematics and Approximation Theory, 329–38. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6393-1_21.

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Thissen, David, and Anne Thissen-Roe. "Factor Score Estimation from the Perspective of Item Response Theory." In Springer Proceedings in Mathematics & Statistics, 171–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43469-4_14.

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Kunihiro, Noboru. "Quantum Factoring Algorithm: Resource Estimation and Survey of Experiments." In International Symposium on Mathematics, Quantum Theory, and Cryptography, 39–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_7.

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Abstract It is known that Shor’s algorithm can break many cryptosystems such as RSA encryption, provided that large-scale quantum computers are realized. Thus far, several experiments for the factorization of the small composites such as 15 and 21 have been conducted using small-scale quantum computers. In this study, we investigate the details of quantum circuits used in several factoring experiments. We then indicate that some of the circuits have been constructed under the condition that the order of an element modulo a target composite is known in advance. Because the order must be unknown in the experiments, they are inappropriate for designing the quantum circuit of Shor’s factoring algorithm. We also indicate that the circuits used in the other experiments are constructed by relying considerably on the target composite number to be factorized.
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Fujii, Tomohiro, and Masao Hirokawa. "A Data Concealing Technique with Random Noise Disturbance and a Restoring Technique for the Concealed Data by Stochastic Process Estimation." In International Symposium on Mathematics, Quantum Theory, and Cryptography, 103–24. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_11.

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Abstract We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.
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Kumar, Sandeep, Ashish Pathak, and Debashis Khan. "Error Estimation." In Mathematical Theory of Subdivision, 171–88. Boca Raton, Florida : CRC Press, [2019]: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315168265-7.

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Mittelhammer, Ron C. "Point Estimation Theory." In Mathematical Statistics for Economics and Business, 363–431. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5022-1_7.

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Wong, Wing Shing, and Stephen S. T. Yau. "The Estimation Algebra of Nonlinear Filtering Systems." In Mathematical Control Theory, 33–65. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1416-8_2.

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Conference papers on the topic "Mathematics Estimation theory"

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Coelho, Carlos A., Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Preface of the “Symposium on Distribution Theory, Estimation and Inference”." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637899.

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Coelho, Carlos A. "Preface of the “2nd symposium on distribution theory, estimation and inference”." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756341.

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Coelho, Carlos A., Filipe J. Marques, and Wolf-Dieter Richter. "Preface of the “5th Symposium on Distribution Theory, Estimation and Inference”." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4951968.

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Coelho, Carlos A. "Preface of the "3rd symposium on distribution theory, estimation and inference"." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4825612.

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Coelho, Carlos A., and Filipe J. Marques. "Preface of the “4th symposium on distribution theory, estimation and inference”." In PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014 (ICNAAM-2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4912747.

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Zhou, Jiao, Xingyu Peng, and Dongchi Yao. "Quantitative Risk Assessment Techniques Based on Uncertainty Theory for Natural Gas Distribution Station." In 2018 12th International Pipeline Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipc2018-78260.

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Pipeline stations, as an important part of long-distance pipeline systems, include lots of facilities which are highly concentrated and always operate continuously. Risk assessment is an important foundation work for the risk management of these stations. Since various uncertainties exist during the quantitative risk assessment (QRA), this paper explores the theories and approaches of QRA for station accidents, and also introduces some specific mathematical theories for quantification and dealing with uncertainties. This paper combines uncertainty theory effectively with the QRA for gas distribution stations, analyzes the uncertain factors in the QRA of gas distribution station, and establishes Bayesian update model for estimating basic events’ failure rates and probabilities of failure on demand based on generic failure data and plant-specific data. And it also offers conversion method among conjugate prior distribution of different types. Besides, probabilistic estimation model is set up by the combination of fuzzy set theory, expert judgments and fuzzy group decision making. The paper builds Fuzzy Bow-Tie quantitative model for distribution station under dependency relationships, and proposes the sensitivity analysis method for the accident model based on fuzzy importance index, fuzzy uncertainty index and minimal cut sets importance index.
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Ramadhani, F. A., S. Nurrohmah, and M. Novita. "Extreme value theory (EVT) application on estimating the distribution of maxima." In INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2016 (ISCPMS 2016): Proceedings of the 2nd International Symposium on Current Progress in Mathematics and Sciences 2016. Author(s), 2017. http://dx.doi.org/10.1063/1.4991260.

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Raviv, Netanel, Siddharth Jain, and Jehoshua Bruck. "What is the Value of Data? on Mathematical Methods for Data Quality Estimation." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174311.

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Averyanova, Yu, A. Averyanov, and F. Yanovsky. "Statistical estimation of polarimetric method potential for dangerous turbulence detection in rain." In 2010 International Conference on Mathematical Methods in Electromagnetic Theory (MMET). IEEE, 2010. http://dx.doi.org/10.1109/mmet.2010.5611383.

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Bibilashvili, L., M. Prishvin, and R. Zaridze. "Estimation and analisys of thermal response of human tissue during EM exposure." In 2010 International Conference on Mathematical Methods in Electromagnetic Theory (MMET). IEEE, 2010. http://dx.doi.org/10.1109/mmet.2010.5611391.

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Reports on the topic "Mathematics Estimation theory"

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Klema, V. Numerical Algorithms and Mathematical Software for Linear Control and Estimation Theory. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada157525.

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Feldgoise, Jacob, and Remco Zwetsloot. Estimating the Number of Chinese STEM Students in the United States. Center for Security and Emerging Technology, October 2020. http://dx.doi.org/10.51593/20200023.

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In recent years, concern has grown about the risks of Chinese nationals studying science, technology, engineering and mathematics (STEM) subjects at U.S. universities. This data brief estimates the number of Chinese students in the United States in detail, according to their fields of study and degree level. Among its findings: Chinese nationals comprise 16 percent of all graduate STEM students and 2 percent of undergraduate STEM students, lower proportions than were previously suggested in U.S. government reports.
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