Relevant bibliographies by topics / Unbiased Estimation of Estimator Variance

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Academic literature on the topic 'Unbiased Estimation of Estimator Variance'

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Published: 8 September 2023

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Journal articles on the topic "Unbiased Estimation of Estimator Variance"

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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, no. 2 (March 17, 2020): 116. http://dx.doi.org/10.25130/j.v25i2.966.

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In this study, the variance compounds parameters of the mixed bi-division variance analysis sample are estimated. This estimation is obtained, by Bayes quadratic unbiased estimator. The second way to estimate variance compounds parameters of a suggested tow-way analysis of variance mixed model with interaction. estimation is done out by the approach called (MINQUÉ). The estimation approach is conducted on true obtained from departments at the college of agriculture/university of Mosul. These data represent the development of growing various kinds of tomato so that the development represents three factors: the first is tomato kind, this is the first factor (H) and the factor of natural fertilizer rate, and this is the second factor (M), and the interaction between the two factors (HM). A random sample is taken from these data in order to get the random linear sample. The elementary values estimated by Bayes unbiased estimator are very much close to those estimated by variance analysis style when compared with the estimated values of the variance estimation parameters done by minimum standard quadratic unbiased estimation. The elementary values represent random linear sample parameters used to estimate minimum quadratic unbiased standard. The elementary values of the estimations are also obtained via analyzing bi-division variance, then these estimations are employed in estimating minimum quadratic unbiased standard. the estimation results by Bayes approach are very similar to those done by variance analysis http://dx.doi.org/10.25130/tjps.25.2020.038
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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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Hamza 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, no. 4 (October 20, 2020): 82. http://dx.doi.org/10.30526/33.4.2512.

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In this paper, we are mainly concerned with estimating cascade reliability model (2+1) based on inverted exponential distribution and comparing among the estimation methods that are used . The maximum likelihood estimator and uniformly minimum variance unbiased estimators are used to get of the strengths and the stress ;k=1,2,3 respectively then, by using the unbiased estimators, we propose Preliminary test single stage shrinkage (PTSSS) estimator when a prior knowledge is available for the scale parameter as initial value due past experiences . The Mean Squared Error [MSE] for the proposed estimator is derived to compare among the methods. Numerical results about conduct of the considered estimator are discussed including the study of mentioned expressions. The numerical results are exhibited and put it in tables.
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Vincent, Odhiambo, Hellen Waititu, and Nyakundi Omwando Cornelious. "Nonparametric Estimation of Error Variance under Simple Random Sampling without Replacement." International Journal of Mathematics And Computer Research 10, no. 10 (October 21, 2022): 2925–33. http://dx.doi.org/10.47191/ijmcr/v10i10.02.

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This study adopts a nonparametric approach in the estimation of a finite population error variance in the setting where the variance is a constant (homoscedastic) using a model-based technique under simple random sampling without replacement (SRSWOR). A mean square analysis of the estimator has been conducted, including the asymptotic behaviour of the estimator and the results show that the asymptotic distribution in a homoscedastic setting is asymptotically unbiased and consistent. The performance of the developed estimator is compared to that of other existing estimators using real data. R statistical software was utilized to analyze data and numerical results presented graphically for selected models.
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Rytgaard, Mette. "Estimation in the Pareto Distribution." ASTIN Bulletin 20, no. 2 (November 1990): 201–16. http://dx.doi.org/10.2143/ast.20.2.2005443.

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AbstractIn the present paper, different estimators of the Pareto parameter α will be proposed and compared to each others.First traditional estimators of α as the maximum likelihood estimator and the moment estimator will be deduced and their statistical properties will be analyzed. It is shown that the maximum likelihood estimator is biased but it can easily be modified to an minimum-variance unbiased estimator of a. But still the coefficient of variance of this estimator is very large.For similar portfolios containing same types of risks we will expect the estimated α-values to be at the same level. Therefore, credibility theory is used to obtain an alternative estimator of α which will be more stable and less sensitive to random fluctuations in the observed losses.Finally, an estimator of the risk premium for an unlimited excess of loss cover will be proposed. It is shown that this estimator is a minimum-variance unbiased estimator of the risk premium. This estimator of the risk premium will be compared to the more traditional methods of calculating the risk premium.
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Pisarenko, V. F., A. A. Lyubushin, V. B. Lysenko, and T. V. Golubeva. "Statistical estimation of seismic hazard parameters: Maximum possible magnitude and related parameters." Bulletin of the Seismological Society of America 86, no. 3 (June 1, 1996): 691–700. http://dx.doi.org/10.1785/bssa0860030691.

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Abstract The problem of statistical estimation of earthquake hazard parameters is considered. The emphasis is on estimation of the maximum regional magnitude, Mmax, and the maximum magnitude, Mmax(T), in a future time interval T and quantiles of its distribution. Two estimators are suggested: an unbiased estimator with the lowest possible variance and a Bayesian estimator. As an illustration, these methods are applied for the estimation of Mmax and related parameters in California and Italy.
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Villanueva, Beatriz, and 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, no. 1 (January 1, 2001): 71–77. http://dx.doi.org/10.1139/x00-138.

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The efficiency of combined interblock-intrablock and intrablock analysis for the estimation of treatment contrasts in alpha designs is compared using Monte-Carlo simulation. The combined estimator considers treatments and replications as fixed effects and blocks as random effects, whereas the intrablock estimator considers treatments, replications, and blocks as fixed effects. The variances of the estimators are used as the criterion for comparison. The combined estimator yields more accurate estimates than the intrablock estimator when the ratio of the block to the error variance is small, especially for designs with the fewest degrees of freedom. The accuracy of both estimators is similar when the ratio of variances is large. The variance of the combined estimator is very close to that of the best linear unbiased estimator except for designs with small number of replicates and families or provenances. Approximations commonly used for the variance of the combined estimator when variances of the random effects are unknown are studied. The downward or negative bias in the estimates of the variance given by the standard approximation used in statistical packages is largest under the conditions in which the combined estimator is more efficient than the intrablock estimator. Estimates of the relative efficiency of combined estimators have an upward bias that can exceed 10% of the true value in small- and middle-sized designs with two or three replicates. In designs with four or more replicates, often used in forest genetics, the bias is negligible.
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Hahn, Ute, and Dietrich Stoyan. "Unbiased stereological estimation of the surface area of gradient surface processes." Advances in Applied Probability 30, no. 4 (December 1998): 904–20. http://dx.doi.org/10.1239/aap/1035228199.

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An unbiased stereological estimator for surface area density is derived for gradient surface processes which form a particular class of non-stationary spatial surface processes. Vertical planar sections are used for the estimation. The variance of the estimator is studied and found to be infinite for certain types of surface processes. A modification of the estimator is presented which exhibits finite variance.
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Hahn, Ute, and Dietrich Stoyan. "Unbiased stereological estimation of the surface area of gradient surface processes." Advances in Applied Probability 30, no. 04 (December 1998): 904–20. http://dx.doi.org/10.1017/s0001867800008715.

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An unbiased stereological estimator for surface area density is derived for gradient surface processes which form a particular class of non-stationary spatial surface processes. Vertical planar sections are used for the estimation. The variance of the estimator is studied and found to be infinite for certain types of surface processes. A modification of the estimator is presented which exhibits finite variance.
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Ng, Set Foong, Pei Eng Ch’ng, Yee Ming Chew, and Kok Shien Ng. "Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties." Scientific Research Journal 11, no. 1 (June 1, 2014): 15. http://dx.doi.org/10.24191/srj.v11i1.5416.

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Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.
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Dissertations / Theses on the topic "Unbiased Estimation of Estimator Variance"

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Kannappa, Sandeep Mavuduru. "Reduced Complexity Viterbi Decoders for SOQPSK Signals over Multipath Channels." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/604300.

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California
High 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.
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Du, Jichang. "Covariate-matched estimator of the error variance in nonparametric regression." Diss., Online access via UMI:, 2007.

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Carlsson, 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.

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Cardoso, João Nuno Martins. "Robust mean variance." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10706.

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Mestrado em Finanças
Este 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.
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Sadeghkhani, Abdolnasser. "Estimation d'une densité prédictive avec information additionnelle." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/11238.

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Dans le contexte de la théorie bayésienne et de théorie de la décision, l'estimation d'une densité prédictive d'une variable aléatoire occupe une place importante. Typiquement, dans un cadre paramétrique, il y a présence d’information additionnelle pouvant être interprétée sous forme d’une contrainte. Cette thèse porte sur des stratégies et des améliorations, tenant compte de l’information additionnelle, pour obtenir des densités prédictives efficaces et parfois plus performantes que d’autres données dans la littérature. Les résultats s’appliquent pour des modèles avec données gaussiennes avec ou sans une variance connue. Nous décrivons des densités prédictives bayésiennes pour les coûts Kullback-Leibler, Hellinger, Kullback-Leibler inversé, ainsi que pour des coûts du type $\alpha-$divergence et établissons des liens avec les familles de lois de probabilité du type \textit{skew--normal}. Nous obtenons des résultats de dominance faisant intervenir plusieurs techniques, dont l’expansion de la variance, les fonctions de coût duaux en estimation ponctuelle, l’estimation sous contraintes et l’estimation de Stein. Enfin, nous obtenons un résultat général pour l’estimation bayésienne d’un rapport de deux densités provenant de familles exponentielles.
Abstract: 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.
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Baba, Harra M'hammed. "Estimation de densités spectrales d'ordre élevé." Rouen, 1996. http://www.theses.fr/1996ROUES023.

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Dans cette thèse nous construisons des estimateurs de la densité spectrale du cumulant, pour un processus strictement homogène et centré, l'espace des temps étant l'espace multidimensionnel, euclidien réel ou l'espace multidimensionnel des nombres p-adiques. Dans cette construction nous avons utilisé la méthode de lissage de la trajectoire et un déplacement dans le temps ou la méthode de fenêtres spectrales. Sous certaines conditions de régularité, les estimateurs proposés sont asymptotiquement sans biais et convergents. Les procédures d'estimation exposées peuvent trouver des applications dans de nombreux domaines scientifiques et peuvent aussi fournir des éléments de réponse aux questions relatives à certaines propriétés statistiques des processus aléatoires.
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Naftali, 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.

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Harti, Mostafa. "Estimation robuste sous un modèle de contamination non symétrique et M-estimateur multidimensionnel." Nancy 1, 1986. http://www.theses.fr/1986NAN10063.

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Dans cette thèse nous étudions la robustesse des estimateurs sous les deux modèles de contamination non symétrique: F::(epsilon ),X=(1-epsilon )F::(theta )+epsilon H::(X) et F::(epsilon )=(1-epsilon )F::(theta )+epsilon G. Nous étudions aussi la robustesse des M-estimateurs multidimensionnels et en particulier les M-estimateurs de régression non linéaire pour lesquels nous établissons la normalité asymptotique
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Krishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks." Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.

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

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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.
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Books on the topic "Unbiased Estimation of Estimator Variance"

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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.

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Optimal unbiased estimation of variance components. Berlin: Springer-Verlag, 1986.

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Newey, Whitney K. Kernel estimation of partial means and a general variance estimator. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1992.

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Ouyang, 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.

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T, Schreuder Hans, Boes Duane C, and 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.

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T, Schreuder Hans, Boes Duane C, and 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.

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Malley, J. D. Optimal unbiased estimation of variance components. Springer, 1986.

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Optimal Unbiased Estimation of Variance Components. Springer, 2012.

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Malley, James D. Optimal Unbiased Estimation of Variance Components. Springer London, Limited, 2012.

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Amina Ali Abd El-Fattah Saleh. Nonlinear unbiased estimators that dominate the intra-block estimator. 1986.

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Book chapters on the topic "Unbiased Estimation of Estimator Variance"

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bias." In Regularized System Identification, 1–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_1.

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AbstractAdopting a quadratic loss, the performance of an estimator can be measured in terms of its mean squared error which decomposes into a variance and a bias component. This introductory chapter contains two linear regression examples which describe the importance of designing estimators able to well balance these two components. The first example will deal with estimation of the means of independent Gaussians. We will review the classical least squares approach which, at first sight, could appear the most appropriate solution to the problem. Remarkably, we will instead see that this unbiased approach can be dominated by a particular biased estimator, the so-called James–Stein estimator. Within this book, this represents the first example of regularized least squares, an estimator which will play a key role in subsequent chapters. The second example will deal with a classical system identification problem: impulse response estimation. A simple numerical experiment will show how the variance of least squares can be too large, hence leading to unacceptable system reconstructions. The use of an approach, known as ridge regression, will give first simple intuitions on the usefulness of regularization in the system identification scenario.
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Malley, James D. "The Algebraic Structure of Variance Components." In 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.

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Malley, James D. "The General Solution to Optimal Unbiased Estimation." In 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.

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Malley, James D. "The Basic Model and The Estimation Problem." In 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.

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Malley, James D. "Statistical Consequences of the Algebraic Structure Theory." In 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.

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Malley, James D. "Concluding Remarks." In 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.

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Malley, James D. "Basic Linear Technique." In 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.

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Malley, James D. "Linearization of the Basic Model." In 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.

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Malley, James D. "The Ordinary Least Squares Estimates." In 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.

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Malley, James D. "The Seely-Zyskind Results." In 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.

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Conference papers on the topic "Unbiased Estimation of Estimator Variance"

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Amini, Mohammadhadi, Arif I. Sarwat, S. S. Iyengar, and Ismail Guvenc. "Determination of the minimum-variance unbiased estimator for DC power-flow estimation." In IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2014. http://dx.doi.org/10.1109/iecon.2014.7048486.

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Spring, Ryan, and Anshumali Shrivastava. "Mutual Information Estimation using LSH Sampling." In 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.

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Learning representations in an unsupervised or self-supervised manner is a growing area of research. Current approaches in representation learning seek to maximize the mutual information between the learned representation and original data. One of the most popular ways to estimate mutual information (MI) is based on Noise Contrastive Estimation (NCE). This MI estimate exhibits low variance, but it is upper-bounded by log(N), where N is the number of samples. In an ideal scenario, we would use the entire dataset to get the most accurate estimate. However, using such a large number of samples is computationally prohibitive. Our proposed solution is to decouple the upper-bound for the MI estimate from the sample size. Instead, we estimate the partition function of the NCE loss function for the entire dataset using importance sampling (IS). In this paper, we use locality-sensitive hashing (LSH) as an adaptive sampler and propose an unbiased estimator that accurately approximates the partition function in sub-linear (near-constant) time. The samples are correlated and non-normalized, but the derived estimator is unbiased without any assumptions. We show that our LSH sampling estimate provides a superior bias-variance trade-off when compared to other state-of-the-art approaches.
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Jensen, Tobias Lindstrom, and Elisabeth De Carvalho. "An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces Based on a Minimum Variance Unbiased Estimator." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9053695.

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Agarwal, Ankush, and Emmanuel Gobet. "Finite variance unbiased estimation of stochastic differential equations." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247930.

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Lee, S. M. S. "Minimum variance unbiased estimation based on bootstrap iterations." In Proceedings 23rd International Conference Information Technology Interfaces. ITI 2001. IEEE, 2001. http://dx.doi.org/10.1109/iti.2001.938024.

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Fillatre, Lionel, and Marc Antonini. "Uniformly minimum variance unbiased estimation for asynchronous event-based cameras." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025834.

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Chen, Kewei, Vijay Gupta, and Yih-Fang Huang. "Minimum variance unbiased estimation in the presence of an adversary." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263658.

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Al-Isawi, Jasim M., Abdulhussein S. AL-Mouel, and Ali Hasan Ali. "Quadratic unbiased estimator of variance components in a multivariate repeated measurements model." In 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.

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Hsieh, Chien-Shu. "Optimal Filtering for Systems with Unknown Inputs Via Unbiased Minimum-Variance Estimation." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.344113.

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Cui, Beibei, Xinmin Song, and Lin Tian. "Unbiased minimum-variance estimation for systems with measurement-delay and unknown inputs." In 2018 Chinese Automation Congress (CAC). IEEE, 2018. http://dx.doi.org/10.1109/cac.2018.8623167.

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Reports on the topic "Unbiased Estimation of Estimator Variance"

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Kott, Phillip S. The Degrees of Freedom of a Variance Estimator in a Probability Sample. RTI Press, August 2020. http://dx.doi.org/10.3768/rtipress.2020.mr.0043.2008.

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Inferences from probability-sampling theory (more commonly called “design-based sampling theory”) often rely on the asymptotic normality of nearly unbiased estimators. When constructing a two-sided confidence interval for a mean, the ad hoc practice of determining the degrees of freedom of a probability-sampling variance estimator by subtracting the number of its variance strata from the number of variance primary sampling units (PSUs) can be justified by making usually untenable assumptions about the PSUs. We will investigate the effectiveness of this conventional and an alternative method for determining the effective degrees of freedom of a probability-sampling variance estimator under a stratified cluster sample.
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Stock, James, and Mark Watson. Asymptotically Median Unbiased Estimation of Coefficient Variance in a Time Varying Parameter Model. Cambridge, MA: National Bureau of Economic Research, August 1996. http://dx.doi.org/10.3386/t0201.

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Kline, Patrick. A Note on Variance Estimation for the Oaxaca Estimator of Average Treatment Effects. Cambridge, MA: National Bureau of Economic Research, January 2014. http://dx.doi.org/10.3386/w19784.

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Cattaneo, Matias D., Richard K. Crump, and Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, July 2023. http://dx.doi.org/10.59576/sr.1068.

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Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process. We provide conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and general hypothesis testing for financial applications. We show that the rate of convergence of the estimator is non-uniform and depends on the beta value of interest. We also show that the widely used Fama-MacBeth variance estimator is asymptotically valid but is conservative in general and can be very conservative in empirically relevant settings. We propose a new variance estimator, which is always consistent and provide an empirical implementation which produces valid inference. In our empirical application we introduce a novel risk factor—a measure of the business credit cycle—and show that it is strongly predictive of both the cross-section and time-series behavior of U.S. stock returns.
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