Relevant bibliographies by topics / Local polynomial regression

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  1. Journal articles
  2. Dissertations / Theses
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Academic literature on the topic 'Local polynomial regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Local polynomial regression"

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Mansyur, Abil, Elmanani Simamora, and Ahmad Ahmad. "Percentile Bootstrap Interval on Univariate Local Polynomial Regression Prediction." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 1 (2023): 160. http://dx.doi.org/10.31764/jtam.v7i1.11752.

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This study offers a new technique for constructing percentile bootstrap intervals to predict the regression of univariate local polynomials. Bootstrap regression uses resampling derived from paired and residual bootstrap methods. The main objective of this study is to perform a comparative analysis between the two resampling methods by considering the nominal coverage probability. Resampling uses a nonparametric bootstrap technique with the return method, where each sample point has an equal chance of being selected. The principle of nonparametric bootstrapping uses the original sample data as
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Ligas, Marcin, and Piotr Banasik. "Local height transformation through polynomial regression." Geodesy and Cartography 61, no. 1 (2012): 3–17. http://dx.doi.org/10.2478/v10277-012-0018-5.

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Abstract The paper presents results of the transformation between two height systems Kronstadt’60 and Kronstadt’86 within the area of Krakow’s district, the latter system being nowadays a part of National Spatial Reference System in Poland. The transformation between the two height systems was carried out based on the well known and frequently applied in geodesy polynomial regression. Despite the fact it is well known and frequently applied it is rather seldom broader tested against the optimal degree of a polynomial function, goodness of fit and its predictive capabilities. In this study some
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Staudenmayer, John, and David Ruppert. "Local polynomial regression and simulation-extrapolation." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 66, no. 1 (2004): 17–30. http://dx.doi.org/10.1046/j.1369-7412.2003.05282.x.

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Huang, Jianhua Z. "Local asymptotics for polynomial spline regression." Annals of Statistics 31, no. 5 (2003): 1600–1635. http://dx.doi.org/10.1214/aos/1065705120.

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Di Marzio, Marco, Agnese Panzera, and Charles C. Taylor. "Local polynomial regression for circular predictors." Statistics & Probability Letters 79, no. 19 (2009): 2066–75. http://dx.doi.org/10.1016/j.spl.2009.06.014.

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Kim, Choongrak, Yonjoo Lee, and Byeong U. Park. "Cook's distance in local polynomial regression." Statistics & Probability Letters 54, no. 1 (2001): 33–40. http://dx.doi.org/10.1016/s0167-7152(01)00031-1.

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Kikechi, Conlet Biketi, and Richard Onyino Simwa. "On Comparison of Local Polynomial Regression Estimators for P=0 and P=1 in a Model Based Framework." International Journal of Statistics and Probability 7, no. 4 (2018): 104. http://dx.doi.org/10.5539/ijsp.v7n4p104.

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This article discusses the local polynomial regression estimator for and the local polynomial regression estimator for in a finite population. The performance criterion exploited in this study focuses on the efficiency of the finite population total estimators. Further, the discussion explores analytical comparisons between the two estimators with respect to asymptotic relative efficiency. In particular, asymptotic properties of the local polynomial regression estimator of finite population total for are derived in a model based framework. The results of the local polynomial regression estimat
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Seifert, Burkhardt, and Theo Gasser. "Data Adaptive Ridging in Local Polynomial Regression." Journal of Computational and Graphical Statistics 9, no. 2 (2000): 338. http://dx.doi.org/10.2307/1390658.

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El Ghouch, Anouar, and Marc G. Genton. "Local Polynomial Quantile Regression With Parametric Features." Journal of the American Statistical Association 104, no. 488 (2009): 1416–29. http://dx.doi.org/10.1198/jasa.2009.tm08400.

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He, Zonglin, and Jean D. Opsomer. "Local polynomial regression with an ordinal covariate." Journal of Nonparametric Statistics 27, no. 4 (2015): 516–31. http://dx.doi.org/10.1080/10485252.2015.1078462.

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Dissertations / Theses on the topic "Local polynomial regression"

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Dharmasena, Tibbotuwa Deniye Kankanamge Lasitha Sandamali, and Sandamali dharmasena@rmit edu au. "Sequential Procedures for Nonparametric Kernel Regression." RMIT University. Mathematical and Geospatial Sciences, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20090119.134815.

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In a nonparametric setting, the functional form of the relationship between the response variable and the associated predictor variables is unspecified; however it is assumed to be a smooth function. The main aim of nonparametric regression is to highlight an important structure in data without any assumptions about the shape of an underlying regression function. In regression, the random and fixed design models should be distinguished. Among the variety of nonparametric regression estimators currently in use, kernel type estimators are most popular. Kernel type estimators provide a flexible c
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Doruska, Paul F. "Methods for Quantitatively Describing Tree Crown Profiles of Loblolly pine (Pinus taeda L.)." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30638.

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Physiological process models, productivity studies, and wildlife abundance studies all require accurate representations of tree crowns. In the past, geometric shapes or flexible mathematical equations approximating geometric shapes were used to represent crown profiles. Crown profile of loblolly pine (<I>Pinus taeda</I> L.) was described using single-regressor, nonparametric regression analysis in an effort to improve crown representations. The resulting profiles were compared to more traditional representations. Nonparametric regression may be applicable when an underlying parametric m
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Geller, Juliane [Verfasser], Michael H. [Gutachter] Neumann, and Gustau [Gutachter] Camps-Valls. "Improved local polynomial estimation in nonparametric time series regression / Juliane Geller ; Gutachter: Michael H. Neumann, Gustau Camps-Valls." Jena : Friedrich-Schiller-Universität Jena, 2017. http://d-nb.info/1177603314/34.

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Bandreddy, Neel Kamal. "Estimation of Unmeasured Radon Concentrations in Ohio Using Quantile Regression Forest." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1418311498.

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Santos, Douglas Gomes dos. "Estimação de volatilidade em séries financeiras : modelos aditivos semi-paramétricos e GARCH." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/14892.

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A estimação e previsão da volatilidade de ativos são de suma importância para os mercados financeiros. Temas como risco e incerteza na teoria econômica moderna incentivaram a procura por métodos capazes de modelar uma variância condicional que evolui ao longo do tempo. O objetivo principal desta dissertação é comparar alguns métodos de regressão global e local quanto à extração da volatilidade dos índices Ibovespa e Standard and Poor´s 500. Para isto, são realizadas estimações e previsões com os modelos GARCH paramétricos e com os modelos aditivos semi-paramétricos. Os primeiros, tradicionalme
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"Local polynomial fitting in nonparametric regression." 1998. http://library.cuhk.edu.hk/record=b6073145.

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Wenyang Zhang.<br>"October 1998."<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (p. 190-196).<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Mode of access: World Wide Web.<br>Abstracts in English and Chinese.
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Weng, Yueh-Tang, and 翁岳塘. "Incremental and Decremental Local Polynomial Regression." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/05473657012193048697.

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碩士<br>國立中興大學<br>統計學研究所<br>104<br>Local polynomial regression (LPR) is a common nonparametric technique. Incremental computing will be more concerned when the era of Internet of Things (IoT) comes. We not only apply incremental computing for LPR and name the process incremental LPR (ILPR), but also propose an approximated incremental LPR (AILPR). The main idea and mathematical skill on AILPR the approach are developed on the approximation in the special case of Woodbury matrix identity. Bandwidth selection is crucial in LPR. To avoid over_tting, one commonly considers a cross-validation (CV), w
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Lin, Shuoh-Huey, and 林碩慧. "Change-Point Estimation By Local Polynomial Regression Smoothers." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/43825923468431820612.

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碩士<br>國立交通大學<br>統計學研究所<br>84<br>Consider the problem of estimating an unknown function that is smooth except for some change-points, where discontinuities occur on either the function or its first-order derivatives. Motivated by some appealing properties of local linear regression estimators, especially of no boundary effects, Shiau and Yeh (1995) proposed a jump-point estimator in the local linear regression context and suggested a back-fitting procedure to estimate the underlying regression function which has some jumps. This article investigates the boundary behaviors of the mean function
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Tilahun, Gelila. "Statistical Methods for Dating Collections of Historical Documents." Thesis, 2011. http://hdl.handle.net/1807/29890.

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The problem in this thesis was originally motivated by problems presented with documents of Early England Data Set (DEEDS). The central problem with these medieval documents is the lack of methods to assign accurate dates to those documents which bear no date. With the problems of the DEEDS documents in mind, we present two methods to impute missing features of texts. In the first method, we suggest a new class of metrics for measuring distances between texts. We then show how to combine the distances between the texts using statistical smoothing. This method can be adapted to settings wh
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Krishnan, Sunder Ram. "Optimum Savitzky-Golay Filtering for Signal Estimation." Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3293.

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Motivated by the classic works of Charles M. Stein, we focus on developing risk-estimation frameworks for denoising problems in both one-and two-dimensions. We assume a standard additive noise model, and formulate the denoising problem as one of estimating the underlying clean signal from noisy measurements by minimizing a risk corresponding to a chosen loss function. Our goal is to incorporate perceptually-motivated loss functions wherever applicable, as in the case of speech enhancement, with the squared error loss being considered for the other scenarios. Since the true risks are observed t
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Books on the topic "Local polynomial regression"

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I, Gijbels, ed. Local polynomial modelling and its applications. Chapman & Hall, 1996.

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Fan, Jianqing. Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66. CRC Press LLC, 2018.

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Fan, Jianqing. Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66. CRC Press LLC, 2018.

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Fan, Jianqing. Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66. CRC Press LLC, 2018.

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Fan, Jianqing. Local Polynomial Modelling and Its Applications: Monographs on Statistics and Applied Probability 66. CRC Press LLC, 2018.

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Book chapters on the topic "Local polynomial regression"

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Fan, Jianqing, and Irene Gijbels. "Local Polynomial Fitting." In Smoothing and Regression. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch9.

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Fan, J., and I. Gijbels. "Framework for local polynomial regression." In Local Polynomial Modelling and its Applications. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3150-4_3.

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Wallin, John F., and Aamer Haque. "LPRH — Local Polynomial Regression Hydrodynamics." In Lecture Notes in Computational Science and Engineering. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-56103-0_27.

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Fan, J., and I. Gijbels. "Local polynomial regression for multivariate data." In Local Polynomial Modelling and its Applications. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3150-4_7.

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Korostelev, Alexander, and Olga Korosteleva. "Local polynomial approximation of regression function." In Graduate Studies in Mathematics. American Mathematical Society, 2011. http://dx.doi.org/10.1090/gsm/119/09.

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Miller, Hugh, and Peter Hall. "Local polynomial regression and variable selection." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2010. http://dx.doi.org/10.1214/10-imscoll615.

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Bickel, Peter J., and Bo Li. "Local polynomial regression on unknown manifolds." In Complex Datasets and Inverse Problems. Institute of Mathematical Statistics, 2007. http://dx.doi.org/10.1214/074921707000000148.

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Adam, Cécile, and Irène Gijbels. "Partially Linear Expectile Regression Using Local Polynomial Fitting." In Advances in Contemporary Statistics and Econometrics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73249-3_8.

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Jian, Haoyu, Zezhi Zeng, Yuping Qian, et al. "The Reconstruction of the Temperature Distribution of a Tubular Solid Oxide Fuel Cell by Surrogate Models." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4856-6_15.

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Abstract It is challenging to directly measure the temperature distribution in solid oxide fuel cell (SOFC) due to their high operating temperature and the difficulty in placing temperature sensors. In this study, we have developed surrogate models to reconstruct the temperature distribution of a tubular SOFC with counter-flow arrangement based on the input of several local temperatures and their axial locations. The surrogate models were trained by the simulation results of 2500 cases. We have compared the accuracy and prediction time for the surrogate model with five different regression alg
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Yixuan, Liu, and J. R. Wishart. "Local Polynomial M-Estimation in Random Design Regression with Dependent Errors." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28665-1_16.

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Conference papers on the topic "Local polynomial regression"

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Sharabati, Walid K., and Bowei Xi. "Fast local polynomial regression approach for speckle noise removal." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7900127.

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Silalahi, Divo Dharma, and Habshah Midi. "Considering a non-polynomial basis for local kernel regression problem." In 2ND INTERNATIONAL CONFERENCE AND WORKSHOP ON MATHEMATICAL ANALYSIS 2016 (ICWOMA2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4972168.

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Trimbach, Ekaterina, Edward Duc Hien Nguyen, and Cesar A. Uribe. "On Acceleration of Gradient-Based Empirical Risk Minimization using Local Polynomial Regression." In 2022 European Control Conference (ECC). IEEE, 2022. http://dx.doi.org/10.23919/ecc55457.2022.9838261.

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Zhang, Z. G., S. C. Chan, X. Zhang, E. Y. Lam, E. X. Wu, and Y. Hu. "High-resolution reconstruction of human brain MRI image based on local polynomial regression." In 2009 4th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2009. http://dx.doi.org/10.1109/ner.2009.5109279.

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Malay, Ivan M., and Evgeniy U. Kharitonov. "Local Polynomial Regression as Adaptive Windowing Function in Complex-Valued Reflection Coefficient Measurements." In 2024 26th International Conference on Digital Signal Processing and its Applications (DSPA). IEEE, 2024. http://dx.doi.org/10.1109/dspa60853.2024.10510145.

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Hany, Umma, and Lutfa Akter. "Local polynomial regression based path loss estimation for weighted centroid localization of endoscopic capsule." In 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC). IEEE, 2017. http://dx.doi.org/10.1109/iccerec.2017.8226694.

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Fibriyani, V., and N. Chamidah. "Prediction of Inflation in Indonesia Using Nonparametric Regression Approach Based on Local Polynomial Estimator." In The 2nd International Seminar on Science and Technology (ISSTEC 2019). Atlantis Press, 2020. http://dx.doi.org/10.2991/assehr.k.201010.013.

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Clark, Caitlyn E., and Bryony DuPont. "Comparing Machine Learning Regression Techniques for Transmission-Related Storm Outages." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85127.

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In this study, we characterize machine learning regression techniques for their ability to predict storm-related transmission outages based on local weather and transmission outage data. To test the machine learning regression techniques, we use data from the central Oregon Coast — which is particularly vulnerable to storm-related transmission outages — for a case study. We test multiple regression methods (linear and polynomial models with varying degrees) as well as support vector regression methods using linear, polynomial, and Radial-Basis-Function kernels. Results indicate relatively poor
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Yue, Hang, Elizabeth G. Jones, and Peter Revesz. "Local Polynomial Regression Models for Average Traffic Speed Estimation and Forecasting in Linear Constraint Databases." In 2010 17th International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2010. http://dx.doi.org/10.1109/time.2010.24.

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Ulya, Millatul, and Nur Chamidah. "Multi-predictor local polynomial regression for predicting the acidity level of avomango (Gadung Klonal 21)." In INTERNATIONAL CONFERENCE ON MATHEMATICS, COMPUTATIONAL SCIENCES AND STATISTICS 2020. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0042290.

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Reports on the topic "Local polynomial regression"

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Pei, Zhuan, David Lee, David Card, and Andrea Weber. Local Polynomial Order in Regression Discontinuity Designs. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w27424.

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Cabrera, Grant. Local Polynomial Regression with Semi-Long Range Dependent Errors. Iowa State University, 2024. http://dx.doi.org/10.31274/cc-20240624-966.

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regressio
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Coughlin, Cletus C., Jeffrey P. Cohen, and John M. Clapp. Local Polynomial Regressions versus OLS for Generating Location Value Estimates: Which is More Efficient in Out-of-Sample Forecasts? Federal Reserve Bank of St. Louis, 2015. http://dx.doi.org/10.20955/wp.2015.014.

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