Literatura académica sobre el tema "Weighted regression estimator"
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Artículos de revistas sobre el tema "Weighted regression estimator"
Kalina, Jan y Jan Tichavský. "On Robust Estimation of Error Variance in (Highly) Robust Regression". Measurement Science Review 20, n.º 1 (1 de febrero de 2020): 6–14. http://dx.doi.org/10.2478/msr-2020-0002.
Texto completoZhou, Xiaoshuang, Xiulian Gao, Yukun Zhang, Xiuling Yin y Yanfeng Shen. "Efficient Estimation for the Derivative of Nonparametric Function by Optimally Combining Quantile Information". Symmetry 13, n.º 12 (10 de diciembre de 2021): 2387. http://dx.doi.org/10.3390/sym13122387.
Texto completoCai, Zongwu. "REGRESSION QUANTILES FOR TIME SERIES". Econometric Theory 18, n.º 1 (febrero de 2002): 169–92. http://dx.doi.org/10.1017/s0266466602181096.
Texto completoKoenker, Roger y Kevin F. Hallock. "Quantile Regression". Journal of Economic Perspectives 15, n.º 4 (1 de noviembre de 2001): 143–56. http://dx.doi.org/10.1257/jep.15.4.143.
Texto completoRahmawati, Dyah P., I. N. Budiantara, Dedy D. Prastyo y Made A. D. Octavanny. "Mixed Spline Smoothing and Kernel Estimator in Biresponse Nonparametric Regression". International Journal of Mathematics and Mathematical Sciences 2021 (11 de marzo de 2021): 1–14. http://dx.doi.org/10.1155/2021/6611084.
Texto completoZhang, Zhengyu. "LOCAL PARTITIONED QUANTILE REGRESSION". Econometric Theory 33, n.º 5 (19 de septiembre de 2016): 1081–120. http://dx.doi.org/10.1017/s0266466616000293.
Texto completoSchreuder, H. T. y Z. Ouyang. "Optimal sampling strategies for weighted linear regression estimation". Canadian Journal of Forest Research 22, n.º 2 (1 de febrero de 1992): 239–47. http://dx.doi.org/10.1139/x92-031.
Texto completoTao, Li, Lingnan Tai, Manling Qian y Maozai Tian. "A New Instrumental-Type Estimator for Quantile Regression Models". Mathematics 11, n.º 15 (4 de agosto de 2023): 3412. http://dx.doi.org/10.3390/math11153412.
Texto completoGlynn, Adam N. y Kevin M. Quinn. "An Introduction to the Augmented Inverse Propensity Weighted Estimator". Political Analysis 18, n.º 1 (2010): 36–56. http://dx.doi.org/10.1093/pan/mpp036.
Texto completoLaome, Lilis, I. Nyoman Budiantara y Vita Ratnasari. "Estimation Curve of Mixed Spline Truncated and Fourier Series Estimator for Geographically Weighted Nonparametric Regression". Mathematics 11, n.º 1 (28 de diciembre de 2022): 152. http://dx.doi.org/10.3390/math11010152.
Texto completoTesis sobre el tema "Weighted regression estimator"
Liu, Yang. "Analysis of Dependently Truncated Sample Using Inverse Probability Weighted Estimator". Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/110.
Texto completoZhang, Zongjun. "Adaptive Robust Regression Approaches in data analysis and their Applications". University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445343114.
Texto completoYang, Yani. "Dimension reduction in the regressions through weighted variance estimation". HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1073.
Texto completoEdlund, Per-Olov. "Preliminary estimation of transfer function weights : a two-step regression approach". Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1989. http://www.hhs.se/efi/summary/291.htm.
Texto completoLeSage, James P. y Manfred M. Fischer. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation". WU Vienna University of Economics and Business, 2007. http://epub.wu.ac.at/3968/1/SSRN%2Did980965.pdf.
Texto completoGaspard, Guetchine. "FLOOD LOSS ESTIMATE MODEL: RECASTING FLOOD DISASTER ASSESSMENT AND MITIGATION FOR HAITI, THE CASE OF GONAIVES". OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1236.
Texto completoZhuofan, Wu. "Proposta de um modelo de regressão binária com resposta contínua aplicado à análise dos dados do SINASC: identificação de fatores de risco para o baixo peso ao nascer". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/17/17139/tde-25032011-122803/.
Texto completoThe objective of this dissertation is to study the applicability of binary regression models for continuous outcomes in the data analysis from SINASC (Brazilian Live Births Information System), analyzing its advantages, limitations and strategies in the estimation of parameters, when identifying the risk factors for low-birth-weight. Many authors have been using data from SINASC to study the variables that are associated with the low-birth-weight. These authors typically use the usual logistic regression model, which analyzes only binary responses (the dependent variable is coded as 1 for low-birth-weight and 0 for otherwise). The regression model with continuous response was proposed and used to study the variables associated with the newborns with higher propensity to a birth weight below the cutoff point of 2500 g, that is, the answer is expressed as a continuous variable. In this situation, an extension method of the traditional model was used in order to enable obtaining more accurate estimates. For the estimation of the parameters from binary regression model with continuous response, the maximum likelihood method was used. The results obtained from the proposed methodology brought these following advantages comparing with the usual model: (A) the proposed regression model was capable for predicting low birth weight with a bettter precision; (B) the proposed model can process the persistent problems of separation present in the conventional models. Thus, the studied method may offer significant contributions to the Public Health, bringing new possibilities for data analysis in this area.
Can, Mutan Oya. "Comparison Of Regression Techniques Via Monte Carlo Simulation". Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605175/index.pdf.
Texto completoBoruvka, Audrey. "Data-driven estimation for Aalen's additive risk model". Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/489.
Texto completo"Weighted quantile regression and oracle model selection". Thesis, 2009. http://library.cuhk.edu.hk/record=b6074984.
Texto completoKeywords: Weighted quantile regression, Adaptive-LASSO, High dimensionality, Model selection, Oracle property, SCAD, DTARCH models.
Under regularity conditions, I establish asymptotic distributions of the proposed estimators, which show that the model selection methods perform as well as if the correct submodels are known in advance. I also suggest an algorithm for fast implementation of the proposed methodology. Simulations are conducted to compare different estimators, and a real example is used to illustrate their performance.
Jiang, Xuejun.
Adviser: Xinyuan Song.
Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2009.
Includes bibliographical references (leaves 86-92).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Libros sobre el tema "Weighted regression estimator"
Toutenburg, Helge. MSE-comparisons between restricted least squares, mixed, and weighted mixed estimators with special emphasize [i.e. emphasis] to nested restrictions. Berlin: Akademie der Wissenschaften der DDR, Karl-Weierstrass-Institut für Mathematik, 1988.
Buscar texto completoCapítulos de libros sobre el tema "Weighted regression estimator"
Mašíček, L. "Consistency of the Least Weighted Squares Regression Estimator". En Theory and Applications of Recent Robust Methods, 183–94. Basel: Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7958-3_17.
Texto completoCrossa, José, J. Jesús Cerón-Rojas, Johannes W. R. Martini, Giovanny Covarrubias-Pazaran, Gregorio Alvarado, Fernando H. Toledo y Velu Govindan. "Theory and Practice of Phenotypic and Genomic Selection Indices". En Wheat Improvement, 593–616. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_32.
Texto completoHeumann, Christian y Shalabh. "Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions". En Recent Advances in Linear Models and Related Areas, 401–16. Heidelberg: Physica-Verlag HD, 2008. http://dx.doi.org/10.1007/978-3-7908-2064-5_22.
Texto completoLu, Dejun, Weifeng Zhang, Kaixuan Cuan y Pengfei Liu. "Reflectance Estimation Based on Locally Weighted Linear Regression Methods". En Communications in Computer and Information Science, 93–101. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1648-7_8.
Texto completoKoul, Hira L. y Pei Geng. "Weighted Empirical Minimum Distance Estimators in Berkson Measurement Error Regression Models". En Analytical Methods in Statistics, 31–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48814-7_3.
Texto completoRiego del Castillo, Virginia, Lidia Sánchez-González, Laura Fernández-Robles, Manuel Castejón-Limas y Rubén Rebollar. "Estimation of Lamb Weight Using Transfer Learning and Regression". En Lecture Notes in Networks and Systems, 23–30. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18050-7_3.
Texto completoMolinero-Parejo, Ramón. "Geographically Weighted Methods to Validate Land Use Cover Maps". En Land Use Cover Datasets and Validation Tools, 255–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90998-7_13.
Texto completoAbdelhady, Aya Salama, Aboul Ella Hassanien, Yasser Mahmoud Awad, Moataz El-Gayar y Aly Fahmy. "Automatic Sheep Weight Estimation Based on K-Means Clustering and Multiple Linear Regression". En Advances in Intelligent Systems and Computing, 546–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99010-1_50.
Texto completoTassinari, Giorgio y Demetrio Panarello. "The effectiveness of marketing tools in a consumer goods market in Italy during the Great Recession (2010-2015)". En Proceedings e report, 105–10. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.20.
Texto completoCho, Wanhyun, Junki Kim, Myung-Hwan Na, Sangkyoon Kim y Hyejin Lee. "Estimation of Weights in Growth Stages of Onions Using Statistical Regression Models and Deep Learning Algorithm". En Advances in Computer Science and Ubiquitous Computing, 159–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9343-7_22.
Texto completoActas de conferencias sobre el tema "Weighted regression estimator"
Zuo, Weibing. "A New Stochastic Restricted Liu Estimator in Weighted Mixed Regression". En 2009 Second International Symposium on Computational Intelligence and Design. IEEE, 2009. http://dx.doi.org/10.1109/iscid.2009.68.
Texto completoPati, Kafi Dano, Robiah Adnan, Bello Abdulkadir Rasheed y Muhammad Alias MD. J. "Estimation parameters using Bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers". En ADVANCES IN INDUSTRIAL AND APPLIED MATHEMATICS: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23). Author(s), 2016. http://dx.doi.org/10.1063/1.4954633.
Texto completoCahyono, Endro Setyo y Muhammad Alifian Nuriman. "Auxiliary information based exponentially weighted moving coefficient of variation control chart using regression estimator (AIB-EWMCVReg)". En TOWARD ADAPTIVE RESEARCH AND TECHNOLOGY DEVELOPMENT FOR FUTURE LIFE. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0114193.
Texto completoKuzairi, N. Chamidah y I. N. Budiantara. "Theoretical Study of Fourier Series Estimator in Semiparametric Regression for Longitudinal Data Based on Weighted Least Square Optimization". En 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.200303.064.
Texto completoBroadie, Mark, Yiping Du y Ciamac C. Moallemi. "Risk estimation via weighted regression". En 2011 Winter Simulation Conference - (WSC 2011). IEEE, 2011. http://dx.doi.org/10.1109/wsc.2011.6148077.
Texto completoVegh, J. y Andrew Milligan. "Revisiting Legacy Weight Relationships Using Machine Learning Techniques". En Vertical Flight Society 78th Annual Forum & Technology Display. The Vertical Flight Society, 2022. http://dx.doi.org/10.4050/f-0078-2022-17494.
Texto completoJuan Luo. "Parameter estimation in geographically weighted regression". En 2009 17th International Conference on Geoinformatics. IEEE, 2009. http://dx.doi.org/10.1109/geoinformatics.2009.5292988.
Texto completoRoh, Myung-Il, Seong-Ho Seo, Hyun-Kyoung Shin, Nam-Kug Ku, Sol Ha y Ki-Su Kim. "Simplified Model for the Weight Estimation of Floating Offshore Plant Using the Statistical Method". En ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24379.
Texto completoParedes, Jose L. y Gonzalo R. Arce. "Compressive sensing signal reconstruction by weighted median regression estimates". En 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495738.
Texto completoLin, Zhipeng. "ML Estimation of Spatial Panel Data Geographically Weighted Regression Model". En 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5999234.
Texto completoInformes sobre el tema "Weighted regression estimator"
Villamizar-Villegas, Mauricio y Yasin Kursat Onder. Uncovering Time-Specific Heterogeneity in Regression Discontinuity Designs. Banco de la República de Colombia, noviembre de 2020. http://dx.doi.org/10.32468/be.1141.
Texto completoKott, Phillip S. The Role of Weights in Regression Modeling and Imputation. RTI Press, abril de 2022. http://dx.doi.org/10.3768/rtipress.2022.mr.0047.2203.
Texto completoGiltinan, D. M., R. J. Carroll y D. Ruppert. Some New Estimation Methods for Weighted Regression When There are Possible Outliers. Fort Belvoir, VA: Defense Technical Information Center, enero de 1985. http://dx.doi.org/10.21236/ada152104.
Texto completoMathew, Sonu, Srinivas S. Pulugurtha y Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, junio de 2022. http://dx.doi.org/10.31979/mti.2022.2119.
Texto completoOver, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong y Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, junio de 2016. http://dx.doi.org/10.36501/0197-9191/16-014.
Texto completoGoetsch, Arthur L., Yoav Aharoni, Arieh Brosh, Ryszard (Richard) Puchala, Terry A. Gipson, Zalman Henkin, Eugene D. Ungar y Amit Dolev. Energy Expenditure for Activity in Free Ranging Ruminants: A Nutritional Frontier. United States Department of Agriculture, junio de 2009. http://dx.doi.org/10.32747/2009.7696529.bard.
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