Relevant bibliographies by topics / And shrinkage estimator (SE)

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

Academic literature on the topic 'And shrinkage estimator (SE)'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "And shrinkage estimator (SE)"

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Zakerzadeh, Hojatollah, Ali Akbar Jafari, and Mahdieh Karimi. "Optimal Shrinkage Estimations for the Parameters of Exponential Distribution Based on Record Values." Revista Colombiana de Estadística 39, no. 1 (2016): 33–44. http://dx.doi.org/10.15446/rce.v39n1.55137.

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<p>This paper studies shrinkage estimation after the preliminary test for the parameters of exponential distribution based on record values. The optimal value of shrinkage coefficients is also obtained based on the minimax regret criterion. The maximum likelihood, pre-test, and shrinkage estimators are compared using a simulation study. The results to estimate the scale parameter show that the optimal shrinkage estimator is better than the maximum likelihood estimator in all cases, and when the prior guess is near the true value, the pre-test estimator is better than shrinkage estimator.
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Saleh, A. K. Md Ehsanes, M. Arashi, M. Norouzirad, and B. M. Goalm Kibria. "On shrinkage and selection: ANOVA model." Journal of Statistical Research 51, no. 2 (2018): 165–91. http://dx.doi.org/10.47302/jsr.2017510205.

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This paper considers the estimation of the parameters of an ANOVA model when sparsity is suspected. Accordingly, we consider the least square estimator (LSE), restricted LSE, preliminary test and Stein-type estimators, together with three penalty estimators, namely, the ridge estimator, subset selection rules (hard threshold estimator) and the LASSO (soft threshold estimator). We compare and contrast the L2-risk of all the estimators with the lower bound of L2-risk of LASSO in a family of diagonal projection scheme which is also the lower bound of the exact L2-risk of LASSO. The result of this
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Hansen, Bruce E. "SHRINKAGE EFFICIENCY BOUNDS." Econometric Theory 31, no. 4 (2014): 860–79. http://dx.doi.org/10.1017/s0266466614000693.

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This paper is an extension of Magnus (2002, Econometrics Journal 5, 225–236) to multiple dimensions. We consider estimation of a multivariate normal mean under sum of squared error loss. We construct the efficiency bound (the lowest achievable risk) for minimax shrinkage estimation in the class of minimax orthogonally invariate estimators satisfying the sufficient conditions of Efron and Morris (1976, Annals of Statistics 4, 11–21). This allows us to compare the regret of existing orthogonally invariate shrinkage estimators. We also construct a new shrinkage estimator which achieves substantia
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Afshari, Mahmoud, and Hamid Karamikabir. "The Location Parameter Estimation of Spherically Distributions with Known Covariance Matrices." Statistics, Optimization & Information Computing 8, no. 2 (2020): 499–506. http://dx.doi.org/10.19139/soic-2310-5070-710.

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This paper presents shrinkage estimators of the location parameter vector for spherically symmetric distributions. We suppose that the mean vector is non-negative constraint and the components of diagonal covariance matrix is known.We compared the present estimator with natural estimator by using risk function.We show that when the covariance matrices are known, under the balance error loss function, shrinkage estimator has the smaller risk than the natural estimator. Simulation results are provided to examine the shrinkage estimators.
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Prakash, Gyan. "Some Estimators for the Pareto Distribution." Journal of Scientific Research 1, no. 2 (2009): 236–47. http://dx.doi.org/10.3329/jsr.v1i2.1642.

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We derive some shrinkage test-estimators and the Bayes estimators for the shape parameter of a Pareto distribution under the general entropy loss (GEL) function. The properties have been studied in terms of relative efficiency. The choices of shrinkage factor are also suggested.  Keywords: General entropy loss; Shrinkage factor; Shrinkage test-estimator; Bayes estimator; Relative efficiency. © 2009 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v1i2.1642 Â
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Kambo, N. S., B. R. Fanda, and Z. A. Al-Hemyari. "On huntseerger type shrinkage estimator." Communications in Statistics - Theory and Methods 21, no. 3 (1992): 823–41. http://dx.doi.org/10.1080/03610929208830817.

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Hassan, N. J., J. Mahdi Hadad, and A. Hawad Nasar. "Bayesian Shrinkage Estimator of Burr XII Distribution." International Journal of Mathematics and Mathematical Sciences 2020 (June 22, 2020): 1–6. http://dx.doi.org/10.1155/2020/7953098.

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In this paper, we derive the generalized Bayesian shrinkage estimator of parameter of Burr XII distribution under three loss functions: squared error, LINEX, and weighted balance loss functions. Therefore, we obtain three generalized Bayesian shrinkage estimators (GBSEs). In this approach, we find the posterior risk function (PRF) of the generalized Bayesian shrinkage estimator (GBSE) with respect to each loss function. The constant formula of GBSE is computed by minimizing the PRF. In special cases, we derive two new GBSEs under the weighted loss function. Finally, we give our conclusion.
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OKHRIN, YAREMA, and WOLFGANG SCHMID. "ESTIMATION OF OPTIMAL PORTFOLIO WEIGHTS." International Journal of Theoretical and Applied Finance 11, no. 03 (2008): 249–76. http://dx.doi.org/10.1142/s0219024908004798.

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The paper discusses finite sample properties of optimal portfolio weights, estimated expected portfolio return, and portfolio variance. The first estimator assumes the asset returns to be independent, while the second takes them to be predictable using a linear regression model. The third and the fourth approaches are based on a shrinkage technique and a Bayesian methodology, respectively. In the first two cases, we establish the moments of the weights and the portfolio returns. A consistent estimator of the shrinkage parameter for the third estimator is then derived. The advantages of the shr
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Carriero, Andrea, George Kapetanios, and Massilimiano Marcellino. "A Shrinkage Instrumental Variable Estimator for Large Datasets." Articles 91, no. 1-2 (2016): 67–87. http://dx.doi.org/10.7202/1036914ar.

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This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available and/or the number of instruments is large relative to the total number of observations. Intuition and recent work (see, e.g., Hahn, 2002) suggest that parsimonious devices used in the construction of the final instruments may provide effective estimation strategies. Shrinkage is a well known approach that promotes parsimony. We consider a new shrinkage 2SLS estimator. We derive a consistency result for this estimator under general conditions, and via Mont
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Al-Zahrani, Bander. "On the Estimation of Reliability of Weighted Weibull Distribution: A Comparative Study." International Journal of Statistics and Probability 5, no. 4 (2016): 1. http://dx.doi.org/10.5539/ijsp.v5n4p1.

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The paper gives a description of estimation for the reliability function of weighted Weibull distribution. The maximum likelihood estimators for the unknown parameters are obtained. Nonparametric methods such as empirical method, kernel density estimator and a modified shrinkage estimator are provided. The Markov chain Monte Carlo method is used to compute the Bayes estimators assuming gamma and Jeffrey priors. The performance of the maximum likelihood, nonparametric methods and Bayesian estimators is assessed through a real data set.
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Dissertations / Theses on the topic "And shrinkage estimator (SE)"

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Hoque, Zahirul. "Improved estimation for linear models under different loss functions." University of Southern Queensland, Faculty of Sciences, 2004. http://eprints.usq.edu.au/archive/00001438/.

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This thesis investigates improved estimators of the parameters of the linear regression models with normal errors, under sample and non-sample prior information about the value of the parameters. The estimators considered are the unrestricted estimator (UE), restricted estimator (RE), shrinkage restricted estimator (SRE), preliminary test estimator (PTE), shrinkage preliminary test estimator (SPTE), and shrinkage estimator (SE). The performances of the estimators are investigated with respect to bias, squared error and linex loss. For the analyses of the risk functions of the estimators, analy
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Mahdi, Tahir Naweed. "Shrinkage estimation in prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq30515.pdf.

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Kim, Tae-Hwan. "The shrinkage least absolute deviation estimator in large samples and its application to the Treynor-Black model /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9901433.

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Chan, Tsz-hin, and 陳子軒. "Hybrid bootstrap procedures for shrinkage-type estimators." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521826.

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In statistical inference, one is often interested in estimating the distribution of a root, which is a function of the data and the parameters only. Knowledge of the distribution of a root is useful for inference problems such as hypothesis testing and the construction of a confidence set. Shrinkage-type estimators have become popular in statistical inference due to their smaller mean squared errors. In this thesis, the performance of different bootstrap methods is investigated for estimating the distributions of roots which are constructed based on shrinkage estimators. Focus is on two shrin
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Remenyi, Norbert. "Contributions to Bayesian wavelet shrinkage." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45898.

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This thesis provides contributions to research in Bayesian modeling and shrinkage in the wavelet domain. Wavelets are a powerful tool to describe phenomena rapidly changing in time, and wavelet-based modeling has become a standard technique in many areas of statistics, and more broadly, in sciences and engineering. Bayesian modeling and estimation in the wavelet domain have found useful applications in nonparametric regression, image denoising, and many other areas. In this thesis, we build on the existing techniques and propose new methods for applications in nonparametric regression, image d
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Vumbukani, Bokang C. "Comparison of ridge and other shrinkage estimation techniques." Master's thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/4364.

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Includes bibliographical references.<br>Shrinkage estimation is an increasingly popular class of biased parameter estimation techniques, vital when the columns of the matrix of independent variables X exhibit dependencies or near dependencies. These dependencies often lead to serious problems in least squares estimation: inflated variances and mean squared errors of estimates unstable coefficients, imprecision and improper estimation. Shrinkage methods allow for a little bias and at the same time introduce smaller mean squared error and variances for the biased estimators, compared to those of
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Mergel, Victor. "Divergence loss for shrinkage estimation, prediction and prior selection." [Gainesville, Fla.] : University of Florida, 2006. http://purl.fcla.edu/fcla/etd/UFE0015678.

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Ullah, Bashir. "Some contributions to positive part shrinkage estimation in various models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ30263.pdf.

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Tang, Tianyuan, and 唐田园. "On uniform consistency of confidence regions based on shrinkage-type estimators." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47152035.

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Serra, Puertas Jorge. "Shrinkage corrections of sample linear estimators in the small sample size regime." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/404386.

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We are living in a data deluge era where the dimensionality of the data gathered by inexpensive sensors is growing at a fast pace, whereas the availability of independent samples of the observed data is limited. Thus, classical statistical inference methods relying on the assumption that the sample size is large, compared to the observation dimension, are suffering a severe performance degradation. Within this context, this thesis focus on a popular problem in signal processing, the estimation of a parameter, observed through a linear model. This inference is commonly based on a linear filter
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Books on the topic "And shrinkage estimator (SE)"

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Fourdrinier, Dominique, William E. Strawderman, and Martin T. Wells. Shrinkage Estimation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02185-6.

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Tsukuma, Hisayuki, and Tatsuya Kubokawa. Shrinkage Estimation for Mean and Covariance Matrices. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1596-5.

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Saleh, A. K. Md Ehsanes. On shrinkage least squares estimation in a parallelism problem. Dept. of Mathematics and Statistics, Carleton University, 1985.

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Improving efficiency by shrinkage: The James-Stein and ridge regression estimators. Marcel Dekker, 1998.

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Trovant, Mike. A numerical model for the estimation of volumetric shrinkage formation in metals casting. National Library of Canada, 1994.

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Misiewicz, John M. Extension of aggregation and shrinkage techniques used in the estimation of Marine Corps Officer attrition rates. Naval Postgraduate School, 1989.

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Read, Robert R. The use of shrinkage techniques in the estimation of attrition rates for large scale manpower models. Naval Postgraduate School, 1988.

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Dickinson, Charles R. Refinement and extension of shrinkage techniques in loss rate estimation of Marine Corps officer manpower models. Naval Postgraduate School, 1988.

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Ottaviano, Victor B. National mechanical estimator. 2nd ed. Fairmont Press, 1996.

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Koenker, Roger W. On Boscovich's estimator. College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.

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Book chapters on the topic "And shrinkage estimator (SE)"

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Chaturvedi, Anoop, Suchita Kesarwani, and Ram Chandra. "Simultaneous Prediction Based on Shrinkage Estimator." In Recent Advances in Linear Models and Related Areas. Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2064-5_10.

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Bondar, James V. "How Much Improvement can a Shrinkage Estimator Give?" In Advances in the Statistical Sciences: Foundations of Statistical Inference. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-4788-7_9.

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Jurečková, Jana, and Xavier Milhaud. "Shrinkage of Maximum Likelihood Estimator of Multivariate Location." In Asymptotic Statistics. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57984-4_25.

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Ahmed, S. Ejaz, T. Quadir, and S. Nkurunziza. "Optimal Shrinkage Estimation." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_430.

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Keener, Robert W. "Empirical Bayes and Shrinkage Estimators." In Theoretical Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-93839-4_11.

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Rudaś, Krzysztof, and Szymon Jaroszewicz. "Shrinkage Estimators for Uplift Regression." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46150-8_36.

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Ahmed, S. Ejaz, S. Chitsaz, and S. Fallahpour. "Optimal Shrinkage Preliminary Test Estimation." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_431.

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Judge, George G. "Shrinkage-Biased Estimation in Econometrics." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_1923-1.

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Judge, George G. "Shrinkage-Biased Estimation in Econometrics." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_1923.

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Lancewicki, Tomer. "Kernel Matrix Regularization via Shrinkage Estimation." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01177-2_94.

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Conference papers on the topic "And shrinkage estimator (SE)"

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Agarwal, Deepak K. "Shrinkage estimator generalizations of Proximal Support Vector Machines." In the eighth ACM SIGKDD international conference. ACM Press, 2002. http://dx.doi.org/10.1145/775047.775073.

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Rucci, Alessio, Stefano Tebaldini, and Fabio Rocca. "SKP-shrinkage estimator for SAR multi-baselines applications." In 2010 IEEE Radar Conference. IEEE, 2010. http://dx.doi.org/10.1109/radar.2010.5494531.

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Pascal, Frederic, and Yacine Chitour. "Shrinkage covariance matrix estimator applied to STAP detection." In 2014 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2014. http://dx.doi.org/10.1109/ssp.2014.6884641.

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Shenoy, H. Vikram, A. P. Vinod, and Cuntai Guan. "Shrinkage estimator based regularization for EEG motor imagery classification." In 2015 10th International Conference on Information, Communications and Signal Processing (ICICS). IEEE, 2015. http://dx.doi.org/10.1109/icics.2015.7459836.

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Breloy, Arnaud, Esa Ollila, and Frederic Pascal. "Spectral Shrinkage of Tyler's $M$-Estimator of Covariance Matrix." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022652.

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Srinath, K. Pavan, and Ramji Venkataramanan. "Cluster-seeking shrinkage estimators." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541418.

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Damasceno, Filipe F. R., Marcelo B. A. Veras, Diego P. P. Mesquita, Joao P. P. Gomes, and Carlos E. F. de Brito. "Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator." In 2016 5th Brazilian Conference on Intelligent Systems (BRACIS). IEEE, 2016. http://dx.doi.org/10.1109/bracis.2016.084.

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Kontos, Kevin, and Gianluca Bontempi. "An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models." In the 2009 ACM symposium. ACM Press, 2009. http://dx.doi.org/10.1145/1529282.1529448.

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Chakraborty, Rudrasis, Yifei Xing, Minxuan Duan, and Stella X. Yu. "C-SURE: Shrinkage Estimator and Prototype Classifier for Complex-Valued Deep Learning." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00048.

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Dimmery, Drew, Eytan Bakshy, and Jasjeet Sekhon. "Shrinkage Estimators in Online Experiments." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2019. http://dx.doi.org/10.1145/3292500.3330771.

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Reports on the topic "And shrinkage estimator (SE)"

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Cheng, Xu, Zhipeng Liao, and Frank Schorfheide. Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities. National Bureau of Economic Research, 2014. http://dx.doi.org/10.3386/w19792.

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Gao, Hong-ye. Choice of Thresholds for Wavelet Shrinkage Estimate of the Spectrum,. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada290168.

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Walsh, Stephen J., and Mark F. Tardiff. Exploration of regularized covariance estimates with analytical shrinkage intensity for producing invertible covariance matrices in high dimensional hyperspectral data. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/1171912.

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Doersksen, R. E., and David L. Malmquist. Weld Shrinkage Study. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada457049.

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Spencer, J. Brock. Cure shrinkage in casting resins. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1170250.

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Poston, Wendy, George Rogers, Carey Priebe, and Jeffrey Solka. Resistive Grid Kernel Estimator (RGKE). Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada253520.

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Padgett, W. J. A Nonparametric Quantile Estimator: Computation. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169939.

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Leung, Chuk L., Daniel A. Scola, Christopher D. Simone, and Parag Katijar. Development of Processable, Low Cure Shrinkage Adhesives. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada411520.

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Lio, Y. L., and W. J. Padgett. A Generalized Quantile Estimator under Censoring. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada188280.

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Brazelton, Sandra. Interactive Time Recursive State Estimator Program. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada189441.

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