Literatura académica sobre el tema "Unbiased Estimation of Estimator Variance"
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Artículos de revistas sobre el tema "Unbiased Estimation of Estimator Variance"
Thanoon, Shaymaa Riyadh. "A comparison between Bayes estimation and the estimation of the minimal unbiased quadratic Standard of the bi-division variance analysis model in the presence of interaction". Tikrit Journal of Pure Science 25, n.º 2 (17 de marzo de 2020): 116. http://dx.doi.org/10.25130/j.v25i2.966.
Texto completoAladeitan, BENEDICTA, Adewale F. Lukman, Esther Davids, Ebele H. Oranye y Golam B. M. Kibria. "Unbiased K-L estimator for the linear regression model". F1000Research 10 (19 de agosto de 2021): 832. http://dx.doi.org/10.12688/f1000research.54990.1.
Texto completoHamza Raheem, Sairan. "Comparison Among Three Estimation Methods to Estimate Cascade Reliability Model (2+1) Based On Inverted Exponential Distribution". Ibn AL- Haitham Journal For Pure and Applied Sciences 33, n.º 4 (20 de octubre de 2020): 82. http://dx.doi.org/10.30526/33.4.2512.
Texto completoVincent, Odhiambo, Hellen Waititu y Nyakundi Omwando Cornelious. "Nonparametric Estimation of Error Variance under Simple Random Sampling without Replacement". International Journal of Mathematics And Computer Research 10, n.º 10 (21 de octubre de 2022): 2925–33. http://dx.doi.org/10.47191/ijmcr/v10i10.02.
Texto completoRytgaard, Mette. "Estimation in the Pareto Distribution". ASTIN Bulletin 20, n.º 2 (noviembre de 1990): 201–16. http://dx.doi.org/10.2143/ast.20.2.2005443.
Texto completoPisarenko, V. F., A. A. Lyubushin, V. B. Lysenko y T. V. Golubeva. "Statistical estimation of seismic hazard parameters: Maximum possible magnitude and related parameters". Bulletin of the Seismological Society of America 86, n.º 3 (1 de junio de 1996): 691–700. http://dx.doi.org/10.1785/bssa0860030691.
Texto completoVillanueva, Beatriz y Javier Moro. "Variance and efficiency of the combined estimator in incomplete block designs of use in forest genetics: a numerical study". Canadian Journal of Forest Research 31, n.º 1 (1 de enero de 2001): 71–77. http://dx.doi.org/10.1139/x00-138.
Texto completoHahn, Ute y Dietrich Stoyan. "Unbiased stereological estimation of the surface area of gradient surface processes". Advances in Applied Probability 30, n.º 4 (diciembre de 1998): 904–20. http://dx.doi.org/10.1239/aap/1035228199.
Texto completoHahn, Ute y Dietrich Stoyan. "Unbiased stereological estimation of the surface area of gradient surface processes". Advances in Applied Probability 30, n.º 04 (diciembre de 1998): 904–20. http://dx.doi.org/10.1017/s0001867800008715.
Texto completoNg, Set Foong, Pei Eng Ch’ng, Yee Ming Chew y Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties". Scientific Research Journal 11, n.º 1 (1 de junio de 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.5416.
Texto completoTesis sobre el tema "Unbiased Estimation of Estimator Variance"
Kannappa, Sandeep Mavuduru. "Reduced Complexity Viterbi Decoders for SOQPSK Signals over Multipath Channels". International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604300.
Texto completoHigh data rate communication between airborne vehicles and ground stations over the bandwidth constrained Aeronautical Telemetry channel is attributed to the development of bandwidth efficient Advanced Range Telemetry (ARTM) waveforms. This communication takes place over a multipath channel consisting of two components - a line of sight and one or more ground reflected paths which result in frequency selective fading. We concentrate on the ARTM SOQPSKTG transmit waveform suite and decode information bits using the reduced complexity Viterbi algorithm. Two different methodologies are proposed to implement reduced complexity Viterbi decoders in multipath channels. The first method jointly equalizes the channel and decodes the information bits using the reduced complexity Viterbi algorithm while the second method utilizes the minimum mean square error equalizer prior to applying the Viterbi decoder. An extensive numerical study is performed in comparing the performance of the above methodologies. We also demonstrate the performance gain offered by our reduced complexity Viterbi decoders over the existing linear receiver. In the numerical study, both perfect and estimated channel state information are considered.
Du, Jichang. "Covariate-matched estimator of the error variance in nonparametric regression". Diss., Online access via UMI:, 2007.
Buscar texto completoCarlsson, Martin. "Variance Estimation of the Calibration Estimator with Measurement Errors in the Auxiliary Information". Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-68928.
Texto completoCardoso, João Nuno Martins. "Robust mean variance". Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10706.
Texto completoEste estudo empírico tem como objectivo avaliar o impacto da estimação robusta nos portefólios de média variância. Isto foi conseguido fazendo uma simulação do comportamento de 15 acções do SP500. Esta simulação inclui dois cenários: um com amostras que seguem uma distribuição normal e outro com amostras contaminadas não normais. Cada cenário inclui 200 reamostragens. O performance dos portefólios estimados usando a máxima verosimilhança (clássicos) e dos portefólios estimados de forma robusta são comparados, resultando em algumas conclusões: Em amostras normais, portefólios robustos são marginalmente menos eficientes que os portefólios clássicos. Contudo, em amostras não normais, os portefólios robustos apresentam um performance muito superior que os portefólios clássicos. Este acréscimo de performance está positivamente correlacionado com o nível de contaminação da amostra. Em suma, assumindo que os retornos financeiros têm uma distribuição não normal, podemos afirmar que os estimadores robustos resultam em portefólios de média variância mais estáveis.
This empirical study's objective is to evaluate the impact of robust estimation on mean variance portfolios. This was accomplished by doing a simulation on the behavior of 15 SP500 stocks. This simulation includes two scenarios: One with normally distributed samples and another with contaminated non-normal samples. Each scenario includes 200 resamples. The performance of maximum likelihood (classical) estimated portfolios and robustly estimated portfolios are compared, resulting in some conclusions: On normally distributed samples, robust portfolios are marginally less efficient than classical portfolios. However, on non-normal samples, robust portfolios present a much higher performance than classical portfolios. This increase in performance is positively correlated with the level of contamination present on the sample. In summary, assuming that financial returns do not present a normal distribution, we can state that robust estimators result in more stable mean variance portfolios.
Sadeghkhani, Abdolnasser. "Estimation d'une densité prédictive avec information additionnelle". Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/11238.
Texto completoAbstract: In the context of Bayesian theory and decision theory, the estimation of a predictive density of a random variable represents an important and challenging problem. Typically, in a parametric framework, usually there exists some additional information that can be interpreted as constraints. This thesis deals with strategies and improvements that take into account the additional information, in order to obtain effective and sometimes better performing predictive densities than others in the literature. The results apply to normal models with a known or unknown variance. We describe Bayesian predictive densities for Kullback--Leibler, Hellinger, reverse Kullback-Leibler losses as well as for α--divergence losses and establish links with skew--normal densities. We obtain dominance results using several techniques, including expansion of variance, dual loss functions in point estimation, restricted parameter space estimation, and Stein estimation. Finally, we obtain a general result for the Bayesian estimator of a ratio of two exponential family densities.
Baba, Harra M'hammed. "Estimation de densités spectrales d'ordre élevé". Rouen, 1996. http://www.theses.fr/1996ROUES023.
Texto completoNaftali, Eran 1971. "First order bias and second order variance of the Maximum Likelihood Estimator with application to multivariate Gaussian data and time delay and Doppler shift estimation". Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/88334.
Texto completoHarti, Mostafa. "Estimation robuste sous un modèle de contamination non symétrique et M-estimateur multidimensionnel". Nancy 1, 1986. http://www.theses.fr/1986NAN10063.
Texto completoKrishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks". Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.
Texto completoTeixeira, Marcos Vinícius. "Estudos sobre a implementação online de uma técnica de estimação de energia no calorímetro hadrônico do atlas em cenários de alta luminosidade". Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/4169.
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Este trabalho tem como objetivo o estudo de técnicas para a estimação da amplitude de sinais no calorímetro de telhas (TileCal) do ATLAS no LHC em cenários de alta luminosidade. Em alta luminosidade, sinais provenientes de colisões adjacentes são observados, ocasionando o efeito de empilhamento de sinais. Neste ambiente, o método COF (do inglês, Constrained Optimal Filter), apresenta desempenho superior ao algoritmo atualmente implementado no sistema. Entretanto, o COF requer a inversão de matrizes para o cálculo da pseudo-inversa de uma matriz de convolução, dificultando sua implementação online. Para evitar a inversão de matrizes, este trabalho apresenta métodos interativos, para a daptação do COF, que resultam em operações matemáticas simples. Baseados no Gradiente Descendente, os resultados demonstraram que os algoritmos são capazes de estimar a amplitude de sinais empilhados, além do sinal de interesse com eficiência similar ao COF. Visando a implementação online, este trabalho apresenta estudos sobre a complexidade dos métodos iterativos e propõe uma arquitetura de processamento em FPGA. Baseado em uma estrutura sequencial e utilizando lógica aritmética em ponto fixo, os resultados demonstraram que a arquitetura desenvolvida é capaz executar o método iterativo, atendendo os requisitos de tempo de processamento exigidos no TileCal.
This work aims at the study of techniques for online energy estimation in the ATLAS hadronic Calorimeter (TileCal) on the LHC collider. During further periods of the LHC operation, signals coming from adjacent collisions will be observed within the same window, producing a signal superposition. In this environment, the energy reconstruction method COF (Constrained Optimal Filter) outperforms the algorithm currently implemented in the system. However , the COF method requires an inversion of matrices and its online implementation is not feasible. To avoid such inversion of matrices, this work presents iteractive methods to implement the COF, resulting in simple mathematical operations. Based on the Gradient Descent, the results demonstrate that the algorithms are capable of estimating the amplitude of the superimposed signals with efficiency similar to COF. In addition, a processing architecture for FPGA implementation is proposed. The analysis has shown that the algorithms can be implemented in the new TilaCal electronics, reaching the processing time requirements.
Libros sobre el tema "Unbiased Estimation of Estimator Variance"
Malley, James D. Optimal Unbiased Estimation of Variance Components. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2.
Texto completoOptimal unbiased estimation of variance components. Berlin: Springer-Verlag, 1986.
Buscar texto completoNewey, Whitney K. Kernel estimation of partial means and a general variance estimator. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1992.
Buscar texto completoOuyang, Zhao. Finite population corrections of the Horvitz-Thompson estimator and their application in estimating the variance of regression estimators. Fort Collins, Colo: U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1997.
Buscar texto completoT, Schreuder Hans, Boes Duane C y Rocky Mountain Forest and Range Experiment Station (Fort Collins, Colo.), eds. Finite population corrections of the Horvitz-Thompson estimator and their application in estimating the variance of regression estimators. Fort Collins, Colo. (3825 E. Mulberry St., Fort Collins 80524): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1997.
Buscar texto completoT, Schreuder Hans, Boes Duane C y Rocky Mountain Forest and Range Experiment Station (Fort Collins, Colo.), eds. Finite population corrections of the Horvitz-Thompson estimator and their application in estimating the variance of regression estimators. Fort Collins, Colo. (3825 E. Mulberry St., Fort Collins 80524): U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, 1997.
Buscar texto completoMalley, J. D. Optimal unbiased estimation of variance components. Springer, 1986.
Buscar texto completoOptimal Unbiased Estimation of Variance Components. Springer, 2012.
Buscar texto completoMalley, James D. Optimal Unbiased Estimation of Variance Components. Springer London, Limited, 2012.
Buscar texto completoAmina Ali Abd El-Fattah Saleh. Nonlinear unbiased estimators that dominate the intra-block estimator. 1986.
Buscar texto completoCapítulos de libros sobre el tema "Unbiased Estimation of Estimator Variance"
Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao y Lennart Ljung. "Bias". En Regularized System Identification, 1–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_1.
Texto completoMalley, James D. "The Algebraic Structure of Variance Components". En Optimal Unbiased Estimation of Variance Components, 99–128. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_9.
Texto completoMalley, James D. "The General Solution to Optimal Unbiased Estimation". En Optimal Unbiased Estimation of Variance Components, 60–69. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_6.
Texto completoMalley, James D. "The Basic Model and The Estimation Problem". En Optimal Unbiased Estimation of Variance Components, 1–10. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_1.
Texto completoMalley, James D. "Statistical Consequences of the Algebraic Structure Theory". En Optimal Unbiased Estimation of Variance Components, 129–37. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_10.
Texto completoMalley, James D. "Concluding Remarks". En Optimal Unbiased Estimation of Variance Components, 138–39. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_11.
Texto completoMalley, James D. "Basic Linear Technique". En Optimal Unbiased Estimation of Variance Components, 11–14. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_2.
Texto completoMalley, James D. "Linearization of the Basic Model". En Optimal Unbiased Estimation of Variance Components, 15–28. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_3.
Texto completoMalley, James D. "The Ordinary Least Squares Estimates". En Optimal Unbiased Estimation of Variance Components, 29–35. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_4.
Texto completoMalley, James D. "The Seely-Zyskind Results". En Optimal Unbiased Estimation of Variance Components, 36–59. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4615-7554-2_5.
Texto completoActas de conferencias sobre el tema "Unbiased Estimation of Estimator Variance"
Amini, Mohammadhadi, Arif I. Sarwat, S. S. Iyengar y Ismail Guvenc. "Determination of the minimum-variance unbiased estimator for DC power-flow estimation". En IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2014. http://dx.doi.org/10.1109/iecon.2014.7048486.
Texto completoSpring, Ryan y Anshumali Shrivastava. "Mutual Information Estimation using LSH Sampling". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/389.
Texto completoJensen, Tobias Lindstrom y Elisabeth De Carvalho. "An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces Based on a Minimum Variance Unbiased Estimator". En ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053695.
Texto completoAgarwal, Ankush y Emmanuel Gobet. "Finite variance unbiased estimation of stochastic differential equations". En 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247930.
Texto completoLee, S. M. S. "Minimum variance unbiased estimation based on bootstrap iterations". En Proceedings 23rd International Conference Information Technology Interfaces. ITI 2001. IEEE, 2001. http://dx.doi.org/10.1109/iti.2001.938024.
Texto completoFillatre, Lionel y Marc Antonini. "Uniformly minimum variance unbiased estimation for asynchronous event-based cameras". En 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025834.
Texto completoChen, Kewei, Vijay Gupta y Yih-Fang Huang. "Minimum variance unbiased estimation in the presence of an adversary". En 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263658.
Texto completoAl-Isawi, Jasim M., Abdulhussein S. AL-Mouel y Ali Hasan Ali. "Quadratic unbiased estimator of variance components in a multivariate repeated measurements model". En PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0093503.
Texto completoHsieh, Chien-Shu. "Optimal Filtering for Systems with Unknown Inputs Via Unbiased Minimum-Variance Estimation". En TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344113.
Texto completoCui, Beibei, Xinmin Song y Lin Tian. "Unbiased minimum-variance estimation for systems with measurement-delay and unknown inputs". En 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623167.
Texto completoInformes sobre el tema "Unbiased Estimation of Estimator Variance"
Kott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, agosto de 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.
Texto completoStock, James y Mark Watson. Asymptotically Median Unbiased Estimation of Coefficient Variance in a Time Varying Parameter Model. Cambridge, MA: National Bureau of Economic Research, agosto de 1996. http://dx.doi.org/10.3386/t0201.
Texto completoKline, Patrick. A Note on Variance Estimation for the Oaxaca Estimator of Average Treatment Effects. Cambridge, MA: National Bureau of Economic Research, enero de 2014. http://dx.doi.org/10.3386/w19784.
Texto completoCattaneo, Matias D., Richard K. Crump y Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, julio de 2023. http://dx.doi.org/10.59576/sr.1068.
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