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Journal articles on the topic 'Local polynomial regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Wang, Dewei, Xichen Mou, Xiang Li, and Xianzheng Huang. "Local polynomial regression for pooled response data." Journal of Nonparametric Statistics 32, no. 4 (2020): 814–37. http://dx.doi.org/10.1080/10485252.2020.1834104.

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12

Wang, Wu, and Ying Sun. "Penalized local polynomial regression for spatial data." Biometrics 75, no. 4 (2019): 1179–90. http://dx.doi.org/10.1111/biom.13077.

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13

Dong, Jianping, and Renfang Jiang. "A boundary kernel for local polynomial regression." Communications in Statistics - Theory and Methods 29, no. 7 (2000): 1549–58. http://dx.doi.org/10.1080/03610920008832562.

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14

Seifert, Burkhardt, and Theo Gasser. "Data Adaptive Ridging in Local Polynomial Regression." Journal of Computational and Graphical Statistics 9, no. 2 (2000): 338–60. http://dx.doi.org/10.1080/10618600.2000.10474884.

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15

Samarov, Daniel V. "The Fast RODEO for Local Polynomial Regression." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1034–52. http://dx.doi.org/10.1080/10618600.2014.949724.

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16

Vilar-Fernández, Juan M., and José A. Vilar-Fernández. "Recursive local polynomial regression under dependence conditions." Test 9, no. 1 (2000): 209–32. http://dx.doi.org/10.1007/bf02595859.

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17

Chen, Kani, and Zhezhen Jin. "Local polynomial regression analysis of clustered data." Biometrika 92, no. 1 (2005): 59–74. http://dx.doi.org/10.1093/biomet/92.1.59.

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18

Hwang, Ruey-Ching. "Local polynomial M-smoothers in nonparametric regression." Journal of Statistical Planning and Inference 126, no. 1 (2004): 55–72. http://dx.doi.org/10.1016/j.jspi.2003.07.009.

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19

Francisco-Fernández, M., and J. M. Vilar-Fernández. "LOCAL POLYNOMIAL REGRESSION ESTIMATION WITH CORRELATED ERRORS." Communications in Statistics - Theory and Methods 30, no. 7 (2001): 1271–93. http://dx.doi.org/10.1081/sta-100104745.

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20

Hall, Peter G., and Jeffrey S. Racine. "Infinite order cross-validated local polynomial regression." Journal of Econometrics 185, no. 2 (2015): 510–25. http://dx.doi.org/10.1016/j.jeconom.2014.06.003.

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21

Chen, Songnian, and Hanghui Zhang. "Binary quantile regression with local polynomial smoothing." Journal of Econometrics 189, no. 1 (2015): 24–40. http://dx.doi.org/10.1016/j.jeconom.2015.06.019.

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22

Isni Rahma, Junaidi, and Iman Setiawan. "Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR." Tadulako Science and Technology Journal 2, no. 2 (2024): 10–16. http://dx.doi.org/10.22487/sciencetech.v2i2.17300.

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Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to antici

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23

Fitri, Fadhilah, and Mawanda Almuhayar. "Comparison of Linear Regression and Polynomial Local Regression in Modeling Prevalence of Stunting." Rangkiang Mathematics Journal 4, no. 1 (2025): 16–23. https://doi.org/10.24036/rmj.v4i1.81.

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Stunting is one of the main focuses of the government in Indonesia. This is because nutritional status is one of the benchmarks of community welfare. Stunting can be influenced by various societal aspects such as health, economy, social status, and education. One factor that is thought to be closely related to stunting is the level of education. Therefore, the prevalence of stunting and the level of education will be modeled; in this case, the mean years of schooling is used. Modeling uses two approaches: parametric through linear regression and nonparametric through local polynomial regressio

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24

Su, Liyun. "Multivariate Local Polynomial Regression with Application to Shenzhen Component Index." Discrete Dynamics in Nature and Society 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/930958.

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This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivar

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25

Utami, Tiani Wahyu, and Aisyah Lahdji. "MODELING OF LOCAL POLYNOMIAL KERNEL NONPARAMETRIC REGRESSION FOR COVID DAILY CASES IN SEMARANG CITY, INDONESIA." MEDIA STATISTIKA 14, no. 2 (2021): 206–15. http://dx.doi.org/10.14710/medstat.14.2.206-215.

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Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which was recently discovered. Coronavirus disease is now a pandemic that occurs in many countries in the world, one of which is Indonesia. One of the cities in Indonesia that has found many COVID cases is Semarang city, located in Central Java. Data on cases of COVID patients in Semarang City which are measured daily do not form a certain distribution pattern. We can build a model with a flexible statistical approach without any assumptions that must be used,

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26

Prewitt, Kathryn, and Sharon Lohr. "Bandwidth selection in local polynomial regression using eigenvalues." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68, no. 1 (2006): 135–54. http://dx.doi.org/10.1111/j.1467-9868.2005.00537.x.

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27

Yuan, Ying, Hongtu Zhu, Weili Lin, and J. S. Marron. "Local polynomial regression for symmetric positive definite matrices." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 74, no. 4 (2012): 697–719. http://dx.doi.org/10.1111/j.1467-9868.2011.01022.x.

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28

Choi, H., Y. K. Lee, B. U. Park, and K. S. Yu. "LOCAL POLYNOMIAL QUASI-LIKELIHOOD REGRESSION ON RANDOM FIELDS." Australian & New Zealand Journal of Statistics 48, no. 4 (2006): 491–506. http://dx.doi.org/10.1111/j.1467-842x.2006.00455.x.

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29

Doksum, Kjell, Derick Peterson, and Alex Samarov. "On variable bandwidth selection in local polynomial regression." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62, no. 3 (2000): 431–48. http://dx.doi.org/10.1111/1467-9868.00242.

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30

Chen, Kani. "Linear minimax efficiency of local polynomial regression smoothers." Journal of Nonparametric Statistics 15, no. 3 (2003): 343–53. http://dx.doi.org/10.1080/1048525031000120233.

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31

Li, Qi, Xuewen Lu, and Aman Ullah. "Multivariate local polynomial regression for estimating average derivatives." Journal of Nonparametric Statistics 15, no. 4-5 (2003): 607–24. http://dx.doi.org/10.1080/10485250310001605450.

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32

Geller, Juliane, and Michael H. Neumann. "Improved local polynomial estimation in time series regression." Journal of Nonparametric Statistics 30, no. 1 (2017): 1–27. http://dx.doi.org/10.1080/10485252.2017.1402118.

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33

Qiu, Peihua, and Brian Yandell. "Local Polynomial Jump-Detection Algorithm in Nonparametric Regression." Technometrics 40, no. 2 (1998): 141–52. http://dx.doi.org/10.1080/00401706.1998.10485196.

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34

Vilar Fernández, Juan M., and Mario Francisco Fernández. "Local polynomial regression smoothers with AR-error structure." Test 11, no. 2 (2002): 439–64. http://dx.doi.org/10.1007/bf02595716.

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35

Lindström, Torgny, Ulla Holst, and Petter Weibring. "Analysis of lidar fields using local polynomial regression." Environmetrics 16, no. 6 (2005): 619–34. http://dx.doi.org/10.1002/env.726.

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36

Kai, Bo, Runze Li, and Hui Zou. "Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 1 (2010): 49–69. http://dx.doi.org/10.1111/j.1467-9868.2009.00725.x.

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37

曹, 志杰. "Disease risk Prediction Based on Logistic Regression and Local Polynomial Regression." Pure Mathematics 13, no. 12 (2023): 3663–75. http://dx.doi.org/10.12677/pm.2023.1312380.

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38

Utami, Tiani Wahyu, Nur Chamidah, and Toha Saifudin. "Platelet Modeling in DHF Patients Using Local Polynomial Semiparametric Regression on Longitudinal Data." JTAM (Jurnal Teori dan Aplikasi Matematika) 8, no. 1 (2024): 231. http://dx.doi.org/10.31764/jtam.v8i1.17427.

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Regression analysis is one of the statistical methods used to model the relationship between response variables and predictor variables. Semiparametric regression is a combination of parametric and nonparametric regression. The estimator used in estimating the semiparametric regression model in this research is the Local Polynomial. Longitudinal data can be found in the health sector, including dengue hemorrhagic fever (DHF) data. The laboratory criteria for indication of DHF is thrombocytopenia. This research aims to obtain platelets model for DHF patients that can be used for forecasting so

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39

Su, Li Yun, and Chun Hua Wang. "Two-Stage Local Polynomial Regression Method for Image Heteroscedastic Noise Removal." Advanced Materials Research 860-863 (December 2013): 2936–39. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2936.

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In this paper, we introduce the extension of local polynomial fitting to the linear heteroscedastic regression model and its applications in digital image heteroscedastic noise removal. For better image noise removal with heteroscedastic energy, firstly, the local polynomial regression is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. Due to non-parametric technique of local polynomial estimation, we do not need to know the heteroscedastic noise function. Therefore, we improve the estimation precis

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40

Cattaneo, Matias D., Rocío Titiunik, and Gonzalo Vazquez-Bare. "Power calculations for regression-discontinuity designs." Stata Journal: Promoting communications on statistics and Stata 19, no. 1 (2019): 210–45. http://dx.doi.org/10.1177/1536867x19830919.

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In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features. rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and varianc

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41

Qiu, Peihua, and Brian Yandell. "A Local Polynomial Jump-Detection Algorithm in Nonparametric Regression." Technometrics 40, no. 2 (1998): 141. http://dx.doi.org/10.2307/1270648.

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42

Turlach, B. A., and M. P. Wand. "Fast Computation of Auxiliary Quantities in Local Polynomial Regression." Journal of Computational and Graphical Statistics 5, no. 4 (1996): 337. http://dx.doi.org/10.2307/1390888.

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43

Gluhovsky, Ilya, and Alexander Gluhovsky. "Smooth Location-Dependent Bandwidth Selection for Local Polynomial Regression." Journal of the American Statistical Association 102, no. 478 (2007): 718–25. http://dx.doi.org/10.1198/016214507000000086.

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44

Gamst, Anthony, Tanya Wolfson, and Barbara Parry. "Local Polynomial Regression Modeling of Human Plasma Melatonin Levels." Journal of Biological Rhythms 19, no. 2 (2004): 164–74. http://dx.doi.org/10.1177/0748730403261630.

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45

Ledolter, Johannes. "Smoothing Time Series with Local Polynomial Regression on Time." Communications in Statistics - Theory and Methods 37, no. 6 (2008): 959–71. http://dx.doi.org/10.1080/03610920701693843.

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46

Masry, Elias, and Jianqing Fan. "Local Polynomial Estimation of Regression Functions for Mixing Processes." Scandinavian Journal of Statistics 24, no. 2 (1997): 165–79. http://dx.doi.org/10.1111/1467-9469.00056.

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47

Durand, Jean-François. "Local polynomial additive regression through PLS and splines: PLSS." Chemometrics and Intelligent Laboratory Systems 58, no. 2 (2001): 235–46. http://dx.doi.org/10.1016/s0169-7439(01)00162-9.

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48

Masry, Elias. "Multivariate regression estimation: Local polynomial fitting for time series." Nonlinear Analysis: Theory, Methods & Applications 30, no. 6 (1997): 3575–81. http://dx.doi.org/10.1016/s0362-546x(97)00415-x.

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49

Opsomer, Jean D., and David Ruppert. "Fitting a bivariate additive model by local polynomial regression." Annals of Statistics 25, no. 1 (1997): 186–211. http://dx.doi.org/10.1214/aos/1034276626.

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50

Turlach, B. A., and M. P. Wand. "Fast Computation of Auxiliary Quantities in Local Polynomial Regression." Journal of Computational and Graphical Statistics 5, no. 4 (1996): 337–50. http://dx.doi.org/10.1080/10618600.1996.10474716.

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