Relevant bibliographies by topics / Estimators

Contents

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

Academic literature on the topic 'Estimators'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Estimators"

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Benkhaled, Abdelkader, Abdenour Hamdaoui, and Mekki Terbeche. "MINIMAX SHRINKAGE ESTIMATORS AND ESTIMATORS DOMINATING THE JAMES-STEIN ESTIMATOR UNDER THE BALANCED LOSS FUNCTION." Eurasian Mathematical Journal 13, no. 2 (2022): 18–36. http://dx.doi.org/10.32523/2077-9879-2022-13-2-18-36.

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Abonazel, Mohamed. "Bias correction methods for dynamic panel data models with fixed effects." International Journal of Applied Mathematical Research 6, no. 2 (May 24, 2017): 58. http://dx.doi.org/10.14419/ijamr.v6i2.7774.

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This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.
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Hamdaoui, Abdenour, Waleed Almutiry, Mekki Terbeche, and Abdelkader Benkhaled. "Comparison of Risk Ratios of Shrinkage Estimators in High Dimensions." Mathematics 10, no. 1 (December 24, 2021): 52. http://dx.doi.org/10.3390/math10010052.

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In this paper, we analyze the risk ratios of several shrinkage estimators using a balanced loss function. The James–Stein estimator is one of a group of shrinkage estimators that has been proposed in the existing literature. For these estimators, sufficient criteria for minimaxity have been established, and the James–Stein estimator’s minimaxity has been derived. We demonstrate that the James–Stein estimator’s minimaxity is still valid even when the parameter space has infinite dimension. It is shown that the positive-part version of the James–Stein estimator is substantially superior to the James–Stein estimator, and we address the asymptotic behavior of their risk ratios to the maximum likelihood estimator (MLE) when the dimensions of the parameter space are infinite. Finally, a simulation study is carried out to verify the performance evaluation of the considered estimators.
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Cahalan, Jennifer A., Jason Gasper, and Jennifer Mondragon. "Catch estimation in the federal trawl fisheries off Alaska: a simulation approach to compare the statistical properties of three trip-specific catch estimators." Canadian Journal of Fisheries and Aquatic Sciences 72, no. 7 (July 2015): 1024–36. http://dx.doi.org/10.1139/cjfas-2014-0347.

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Quantifying catch has been recognized worldwide as a critical component in fisheries management. Assessment of discard is challenging because of the requirement for at-sea observation, which is both logistically difficult and costly to fishery agencies. Statistical estimators using robust sampling methods may yield accurate and imprecise estimates given the variability associated with many at-sea discard species and inability for agencies to obtain high sampling fractions. However, biased estimates occur if an inappropriate estimator is used. Using Alaska trawl fisheries as an example, we investigated the statistical properties and implementation issues for three commonly used estimators: the ratio estimator; a simple mean estimator; and a deterministic imputation method currently in use in federal fisheries off Alaska. We used a simulation approach to evaluate the performance of these estimators to estimate trip-specific catch. Several statistical properties were evaluated: bias of the estimators, variability of the estimators, and accuracy of the variance estimators. The simple mean estimator had the best performance for vessels landing catch at shoreside processors. The choice of estimator was less clear for vessels processing catch, owing to sensitivity associated with species composition and implementation issues for the simple mean and ratio estimators.
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Hinrichsen, Richard A. "The accuracy of alternative stochastic growth rate estimates for salmon populations." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 6 (June 1, 2002): 1014–23. http://dx.doi.org/10.1139/f02-065.

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The accuracies of four alternative estimators of stochastic growth rate for salmon populations are examined using bootstrapping. The first estimator is based on a stochastic Leslie matrix model that uses age-specific spawner counts. The other three estimators use spawner counts with limited age-structure information: a Botsford–Brittnacher model method and two diffusion approximation methods, namely, the least squares approach of Dennis and the robust approach of Holmes. Accuracy of the estimators was quantified using median bias and interquartile ranges of the stochastic growth rate estimates. The Botsford–Brittnacher estimator was found to be unreliable due to large bias. Of the remaining estimators, the stochastic Leslie approach tended to produce the most reliable estimates but had the greatest data demands. With severe lognormal measurement error, the Dennis estimators produced less biased estimates than the other methods, but precision of the stochastic growth rate was generally highest using the stochastic Leslie estimator.
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Payandeh, Bijan, and Alan R. Ek. "Distance methods and density estimators." Canadian Journal of Forest Research 16, no. 5 (October 1, 1986): 918–24. http://dx.doi.org/10.1139/x86-163.

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The relative performance of five distance–density estimators was evaluated for the n-tree circular, semicircular, and strip plots on several simulated and natural tree populations. Results indicate that for the n-tree circular plot, the ratio estimator performed very well for most populations examined and for n > 10. The performance of both the maximum likelihood and first moment estimators was affected to a great degree by the spatial pattern of the populations, but they performed satisfactorily for the random and uniform populations and for large n values (i.e., n > 10). Smaltschinski's estimator resulted in nearly bias-free estimates for nonaggregated populations and for all n, but performed poorly otherwise. The generalized Prodan estimator performed well for the random population, but overestimated the density otherwise. The relative performance of all estimators for the n-tree semicircular plot was quite similar to that of estimators for the n-tree circular plot, except that the former tended to produce lower density estimates. For n-tree strip plots, the generalized Prodan and the ratio estimator performed very well for the nonaggregated populations and for n > 10. All other estimators resulted in density estimates lower than those for n-tree circular and semicircular plots.
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M. Elgohary, Mervat, Mohamed R. Abonazel, Nahed M. Helmy, and Abeer R. Azazy. "New robust-ridge estimators for partially linear model." International Journal of Applied Mathematical Research 8, no. 2 (November 17, 2019): 46. http://dx.doi.org/10.14419/ijamr.v8i2.29932.

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This paper considers the partially linear model when the explanatory variables are highly correlated as well as the dataset contains outliers. We propose new robust biased estimators for this model under these conditions. The proposed estimators combine least trimmed squares and ridge estimations, based on the spline partial residuals technique. The performance of the proposed estimators and the Speckman-spline estimator has been examined by a Monte Carlo simulation study. The results indicated that the proposed estimators are more efficient and reliable than the Speckman-spline estimator.
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Little, Roderick J., and Roger J. Lewis. "Estimands, Estimators, and Estimates." JAMA 326, no. 10 (September 14, 2021): 967. http://dx.doi.org/10.1001/jama.2021.2886.

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Beauducel, André, Christopher Harms, and Norbert Hilger. "Reliability Estimates for Three Factor Score Estimators." International Journal of Statistics and Probability 5, no. 6 (October 26, 2016): 94. http://dx.doi.org/10.5539/ijsp.v5n6p94.

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Estimates for the reliability of Thurstone’s regression factor score estimator, Bartlett’s factor score estimator, and McDonald’s factor score estimator were proposed. Moreover, conditions for equal reliability of the factor score estimators were presented and the reliability estimates were compared by means of simulation studies. Under conditions inducing unequal reliabilities, reliability estimates were largest for the regression score estimator and lowest for McDonald’s factor score estimator. We provide an R-script and an SPSS-script for the computation of the respective reliability estimates.
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Khan, Dost Muhammad, Muhammad Ali, Zubair Ahmad, Sadaf Manzoor, and Sundus Hussain. "A New Efficient Redescending M-Estimator for Robust Fitting of Linear Regression Models in the Presence of Outliers." Mathematical Problems in Engineering 2021 (November 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/3090537.

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Robust regression is an important iterative procedure that seeks analyzing data sets that are contaminated with outliers and unusual observations and reducing their impact over regression coefficients. Robust estimation methods have been introduced to deal with the problem of outliers and provide efficient and stable estimates in their presence. Various robust estimators have been developed in the literature to restrict the unbounded influence of the outliers or leverage points on the model estimates. Here, a new redescending M-estimator is proposed using a novel objective function with the prime focus on getting highly robust and efficient estimates that give promising results. It is evident from the results that, for normal and clean data, the proposed estimator is almost as efficient as ordinary least square method and, however, becomes highly resistant to outliers when it is used for contaminated datasets. The simulation study is being carried out to assess the performance of the proposed redescending M-estimator over different data generation scenarios including normal, t-distribution, and double exponential distributions with different levels of outliers’ contamination, and the results are compared with the existing redescending M-estimators, e.g., Huber, Tukey Biweight, Hampel, and Andrew-Sign function. The performance of the proposed estimators was also checked using real-life data applications of the estimators and found that the proposed estimators give promising results as compared to the existing estimators.
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Dissertations / Theses on the topic "Estimators"

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Königkrämer, Sören. "Realised volatility estimators." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/8526.

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This dissertation is an investigation into realised volatility (RV) estimators. Here, RV is defined as the sum-of-squared-returns (SSR) and is a proxy for integrated volatility (IV), which is unobservable. The study focuses on a subset of the universe of RV estimators. We examine three categories of estimators: historical, high-frequency (HF) and implied. The need to estimate RV is predominantly in the hedging of options and is not concerned with speculation or forecasting. The main research questions are; (1) what is the best RV estimator in a historical study of S&P 500 data? (2) What is the best RV estimator in a Monte Carlo simulation when delta hedging synthetic options? (3) Do our findings support the stylized fact of `Asymmetry in time scales' (Cont, 2001)? In the answering of these questions, further avenues of investigation are explored. Firstly, the VIX is used as the implied volatility. Secondly, the Monte Carlo simulation generates stock price paths with random components in the stock price and the volatility at each time point. The distribution of the input volatility is varied. The question of asymmetry in time scales is addressed by varying the term and frequency of historical data. The results of the historical and Monte Carlo simulation are compared. The SSR and two of the HF estimators perform best in both cases. Accuracy of estimators using long term data is shown to perform very poorly.
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Vicinansa, Guilherme Scabin. "Algebraic estimators with applications." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3139/tde-21092018-150106/.

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In this work we address the problem of friction compensation in a pneumatic control valve. It is proposed a nonlinear control law that uses algebraic estimators in its structure, in order to adapt the controller to the aging of the valve. For that purpose we estimate parameters related to the valve\'s Karnopp model, necessary to friction compensation, online. The estimators and the controller are validated through simulations.
Nessa pesquisa, estudamos o problema de compensação de atrito em válvulas pneumáticas. É proposta uma lei de controle não linear que tem estimadores algébricos em sua estrutura, para adaptar o controlador ao envelhecimento da válvula. Para isso, estimam-se os valores de parâmetros relacionados ao modelo de Karnopp da válvula, necessários à compensação do atrito, de maneira online. Os estimadores e o controlador são validados através de simulações.
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Zhan, Yihui. "Bootstrapping functional M-estimators /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/8958.

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Tayade, Rajeshwary. "Robustness analysis of linear estimators." Texas A&M University, 2004. http://hdl.handle.net/1969.1/500.

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Robustness of a system has been defined in various ways and a lot of work has been done to model the system robustness , but quantifying or measuring robustness has always been very difficult. In this research we consider a simple system of a linear estimator and then attempt to model the system performance and robustness in a geometrical manner which admits an analysis using the differential geometric concepts of slope and curvature. We try to compare two different types of curvatures, namely the curvature along the maximum slope of a surface and the square-root of the absolute value of sectional curvature of a surface, and observe the values to see if both of them can alternately be used in the process of understanding or measuring system robustness. In this process we have worked on two different examples and taken readings for many points to find if there is any consistency in the two curvatures.
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Rankin, Robert. "Hierarchical models and shrinkage estimators." Thesis, Rankin, Robert (2017) Hierarchical models and shrinkage estimators. PhD thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/38257/.

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Capture-Mark-Recapture (CMR) models are a large class of hierarchical time-series models for estimating the abundance and survival of individually marked animals. Due to the complex multi-parameter nature of CMR models, CMR practitioners have been enthusiastic adopters of multi-model inference (MMI) techniques. By MMI, I refer loosely to a variety of techniques such as model-selection, model-averaging, Frequentist shrinkage estimators, and Hierarchical Bayesian random-e_ects. In this thesis, I develop and compare methods of MMI in CMR, with application to the movement and abundance of bottlenose dolphins, Tursiops sp., in Australia. I use novel ideas from the _eld of machine learning, as well as revisit old estimation problems like the Marginal Likelihood. As this thesis will show, there are many practical problems to the popular AIC/BIC-based methods in odontocetes CMR studies, such as singularities and boundary-value estimates. This is especially the case when there is a lot of demographic and/or temporal variation. Understanding such heterogeneity is important for conservation and ecological theory, such as the role of individual heterogeneity for estimating abundance (Chapter 3), or sex/age di_erences in movement patterns (Chapter 4). Such heterogeneity can also lead to severe non-identiability problems. I suggest practical solutions through HB models and shrinkage estimators. Chapter 2 reviews MMI theory and presents a new boosting algorithm for the Cormack-Jolly-Seber model. Chapter 3 presents a Hierarchical Bayesian (HB) version of Pollock's Closed Robust Design (PCRD), with emphasis on shrinkage priors and individual heterogeneity. Chapter 4 reviews Bayesian model selection and introduces a technique to estimate Marginal Likelihoods and Bayes Factors for hidden-Markov models, such as the PCRD. Chapter 5 generalizes the HB PCRD into a Multistate CRD model, with emphasis on shrinkage priors for inference on geographic state-transitions.
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Ogawa, James S. "Evaluating color fused image performance estimators." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA340997.

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Thesis (M.S. in Operations Research) Naval Postgraduate School, September 1997.
"September 1997." Thesis advisor(s): William K. Krebs. Includes bibliographical references (p. 241-243). Also available online.
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Völcker, Björn. "Performance Analysis of Parametric Spectral Estimators." Doctoral thesis, KTH, Signals, Sensors and Systems, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3323.

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Chan, Eric Wai Chi. "Novel motion estimators for video compression." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6864.

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In this thesis, the problem of motion estimation is addressed from two perspectives, namely, hardware architecture and reduced complexity algorithms in the spatial and transform domains. First, a VLSI architecture which implements the full search block matching algorithm in real time is presented. The interblock dependency is exploited and hence the architecture can meet the real time requirement in various applications. Most importantly, the architecture is simple, modular and cascadable. Hence the proposed architecture is easily implementable in VLSI as a codec. The spatial domain algorithm consists of a layered structure and alleviates the local optimum problem. Most importantly, it employs a simple matching criterion, namely, a modified pixel difference classification (MPDC) and hence results in a reduced computational complexity. In addition, the algorithm is compatible with the recently proposed MPEG-1 video compression standard. Simulation results indicate that the proposed algorithm provides a comparable performance (compared to the algorithms reported in the literature) at a significantly reduced computational complexity. In addition, the hardware implementation of the proposed algorithm is very simple because of the binary operations used in the matching criteria. Finally, we present a wavelet transform based fast multiresolution motion estimation (FMRME) scheme. Here, the wavelet transform is used to exploit both the spatial and temporal redundancies resulting in an efficient coder. In FMRME, the correlations among the orientation subimages of the wavelet pyramid structure are exploited resulting in an efficient motion estimation process. In addition, this significantly reduces side information for motion vectors which corresponds to significant improvements in coding performance of the FMRME based wavelet coder for video compression. Simulation results demonstrate the superior coding performance of the FMRME based wavelet transform coder. (Abstract shortened by UMI.)
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Korsell, Nicklas. "Statistical Properties of Preliminary Test Estimators." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7155.

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Shafie, Termeh. "Design-based estimators for snowball sampling." Stockholms universitet, Statistiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-88948.

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Snowball sampling, where existing study subjects recruit further subjects from amongtheir acquaintances, is a popular approach when sampling from hidden populations.Since people with many in-links are more likely to be selected, there will be a selectionbias in the samples obtained. In order to eliminate this bias, the sample data must beweighted. However, the exact selection probabilities are unknown for snowball samplesand need to be approximated in an appropriate way. This paper proposes differentways of approximating the selection probabilities and develops weighting techniquesusing the inverse of the selection probabilities. Some numerical examples for smallgraphs and simulations on larger networks are provided to compare the efficiencyof the weighting techniques. The simulation results indicate that the suggested re-weighted estimators should be preferred to traditional estimators with equal sampleweights for the initial snowball sampling waves.
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Books on the topic "Estimators"

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Matasov, A. I. Estimators for uncertain dynamic systems. Dordrecht: Kluwer Academic Publishers, 1998.

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Praestgaard, Jens. Nonparametric estimators of actuarial values. Copenhagen: University of Copenhagen, 1989.

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Gruber, Marvin H. J. Regression estimators: A comparative study. Boston: Academic Press, 1992.

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Voinov, V. G., and M. S. Nikulin. Unbiased Estimators and Their Applications. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1970-2.

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Matasov, A. I. Estimators for Uncertain Dynamic Systems. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5322-5.

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Bilodeau, M. Stein estimators under elliptical distributions. [Toronto: University of Toronto, Department of Statistics, 1985.

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Gruber, Marvin H. J. Regression estimators: A comparative study. Boston: Academic Press, 1990.

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Gruber, Marvin H. J. Regression estimators: A comparative study. 2nd ed. Baltimore: Johns Hopkins University Press, 2010.

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Inoue, Atsushi. Two-sample instrumental variables estimators. Cambridge, MA: National Bureau of Economic Research, 2005.

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Matasov, A. I. Estimators for Uncertain Dynamic Systems. Dordrecht: Springer Netherlands, 1998.

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Book chapters on the topic "Estimators"

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Sarang, Poornachandra. "Estimators." In Artificial Neural Networks with TensorFlow 2, 231–90. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6150-7_6.

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Eggermont, Paul P. B., and Vincent N. LaRiccia. "Kernel Estimators." In Springer Series in Statistics, 99–143. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b12285_3.

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van der Vaart, Aad W., and Jon A. Wellner. "M-Estimators." In Weak Convergence and Empirical Processes, 284–308. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2545-2_28.

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van der Vaart, Aad W., and Jon A. Wellner. "Z-Estimators." In Weak Convergence and Empirical Processes, 309–20. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2545-2_29.

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Shiryayev, A. N. "Unbiased Estimators." In Selected Works of A. N. Kolmogorov, 369–94. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2260-3_38.

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Nguyen, Hung T., and Gerald S. Rogers. "Efficient Estimators." In Springer Texts in Statistics, 105–12. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8914-9_17.

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Nguyen, Hung T., and Gerald S. Rogers. "Consistent Estimators." In Springer Texts in Statistics, 149–56. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8914-9_23.

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Nguyen, Hung T., and Gerald S. Rogers. "Equivariant Estimators." In Springer Texts in Statistics, 313–18. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8914-9_44.

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Dekking, Frederik Michel, Cornelis Kraaikamp, Hendrik Paul Lopuhaä, and Ludolf Erwin Meester. "Unbiased estimators." In A Modern Introduction to Probability and Statistics, 285–97. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-168-7_19.

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Tsybakov, Alexandre B. "Nonparametric estimators." In Springer Series in Statistics, 1–76. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-79052-7_1.

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Conference papers on the topic "Estimators"

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Baier, Hendrik, and Michael Kaisers. "ME-MCTS: Online Generalization by Combining Multiple Value Estimators." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/555.

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This paper addresses the challenge of online generalization in tree search. We propose Multiple Estimator Monte Carlo Tree Search (ME-MCTS), with a two-fold contribution: first, we introduce a formalization of online generalization that can represent existing techniques such as "history heuristics", "RAVE", or "OMA" -- contextual action value estimators or abstractors that generalize across specific contexts. Second, we incorporate recent advances in estimator averaging that enable guiding search by combining the online action value estimates of any number of such abstractors or similar types of action value estimators. Unlike previous work, which usually proposed a single abstractor for either the selection or the rollout phase of MCTS simulations, our approach focuses on the combination of multiple estimators and applies them to all move choices in MCTS simulations. As the MCTS tree itself is just another value estimator -- unbiased, but without abstraction -- this blurs the traditional distinction between action choices inside and outside of the MCTS tree. Experiments with three abstractors in four board games show significant improvements of ME-MCTS over MCTS using only a single abstractor, both for MCTS with random rollouts as well as for MCTS with static evaluation functions. While we used deterministic, fully observable games, ME-MCTS naturally extends to more challenging settings.
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Cheng, Heng-Tze, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, et al. "TensorFlow Estimators." In KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3097983.3098171.

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Huijgens, Hennie, and Frank Vogelezang. "Do estimators learn?" In the 7th International Workshop. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2897695.2897698.

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Ceylan, Demet, and Bruce W. Schmeiser. "Interfaced variance estimators." In the 25th conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/256563.257079.

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Lin, Haotong, Sida Peng, Zhize Zhou, and Xiaowei Zhou. "Learning to Estimate Object Poses without Real Image Annotations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/162.

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This paper presents a simple yet effective approach for learning 6DoF object poses without real image annotations. Previous methods have attempted to train pose estimators on synthetic data, but they do not generalize well to real images due to the sim-to-real domain gap and produce inaccurate pose estimates. We find that, in most cases, the synthetically trained pose estimators are able to provide reasonable initialization for depth-based pose refinement methods which yield accurate pose estimates. Motivated by this, we propose a novel learning framework, which utilizes the accurate results of depth-based pose refinement methods to supervise the RGB-based pose estimator. Our method significantly outperforms previous self-supervised methods on several benchmarks. Even compared with fully-supervised methods that use real annotated data, we achieve competitive results without using any real annotation. The code is available at https://github.com/zju3dv/pvnet-depth-sup.
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Song, Woo-Jin, and William A. Pearlman. "Image restoration with a minimum-error minimum-correlation estimator." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.wj39.

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Current image estimators have the property that the estimation error or noise has positive correlation with the true object. In principle, therefore, the estimation noise can be further processed to extract more information about the object. The estimators therefore fall short of the goal of extracting as much information as possible about the object. To remove this deficiency, a new estimator is proposed which attempts simultaneously to maximize the correlation between the estimate and the object (or minimize the correlation between the error and object) and minimize the mean-square error. It turns out that this is an optimization problem with competing objectives. The solution is a compromise between the usual minimum mean-square error (Wiener) estimator (which produces an error uncorrelated with the image) and an inverse filter. The resulting new estimator (which is called MEMO for minimum-error minimum-correlation) is applied to images degraded by linear space-invariant blur and additive noise. Simulations are undertaken with different images having different extents of blur and degrees of noise degradation, where object estimates are obtained through the new MEMO estimator and, for comparison, the Wiener estimator. The MEMC estimator produced sharper and clearer restorations, especially at the lower signal-to-noise ratios.
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Santhanam, N. P., M. M. Madiman, and A. D. Sarwate. "Redundancy of exchangeable estimators." In 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2010. http://dx.doi.org/10.1109/allerton.2010.5707041.

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Bella, Antonio, Cesar Ferri, Jose Hernandez-Orallo, and Maria Jose Ramirez-Quintana. "Quantification via Probability Estimators." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.75.

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Agamennoni, G., P. Furgale, and R. Siegwart. "Self-tuning M-estimators." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139840.

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Alam, Sheikh K., Frederick L. Lizzi, Ernest J. Feleppa, and T. Varghese. "Novel estimators for elastography." In Medical Imaging 2001, edited by Michael F. Insana and K. Kirk Shung. SPIE, 2001. http://dx.doi.org/10.1117/12.428207.

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Reports on the topic "Estimators"

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Hahn, Jinyong, Xiaohong Chen, and Daniel Ackerberg. A practical asymptotic variance estimator for two-step semiparametric estimators. Institute for Fiscal Studies, June 2011. http://dx.doi.org/10.1920/wp.cem.2011.2211.

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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.
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Inoue, Atsushi, and Gary Solon. Two-Sample Instrumental Variables Estimators. Cambridge, MA: National Bureau of Economic Research, June 2005. http://dx.doi.org/10.3386/t0311.

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Nolen, Steven Douglas, Terry R. Adams, and Jeremy Ed Sweezy. Integral Criticality Estimators in MCATK. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1257122.

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Chen, Xiaohong, David Jacho-Chávez, and Oliver Linton. Averaging of moment condition estimators. Institute for Fiscal Studies, September 2012. http://dx.doi.org/10.1920/wp.cem.2012.2612.

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Gee, D. M. Comparison of Dam Breach Parameter Estimators. Fort Belvoir, VA: Defense Technical Information Center, January 2008. http://dx.doi.org/10.21236/ada519246.

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Nolen, Steven D., Terry R. Adams, and Jeremy Ed Sweezy. Integral Criticality Estimators in MCATK(U). Office of Scientific and Technical Information (OSTI), October 2012. http://dx.doi.org/10.2172/1053524.

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Abadie, Alberto, and Guido Imbens. A Martingale Representation for Matching Estimators. Cambridge, MA: National Bureau of Economic Research, February 2009. http://dx.doi.org/10.3386/w14756.

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Salanie, Bernard, and Dennis Kristensen. Higher-order properties of approximate estimators. Cemmap, September 2013. http://dx.doi.org/10.1920/wp.cem.2013.4513.

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Ichimura, Hidehiko, and Whitney K. Newey. The influence function of semiparametric estimators. Cemmap, August 2015. http://dx.doi.org/10.1920/wp.cem.2015.4415.

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