Relevant bibliographies by topics / Nadaraya-Watson estimator

Academic literature on the topic 'Nadaraya-Watson estimator'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Nadaraya-Watson estimator.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Nadaraya-Watson estimator"

1

Lamusu, Febriolah, Tedy Machmud, and Resmawan Resmawan. "Estimator Nadaraya-Watson dengan Pendekatan Cross Validation dan Generalized Cross Validation untuk Mengestimasi Produksi Jagung." Indonesian Journal of Applied Statistics 3, no. 2 (January 23, 2021): 85. http://dx.doi.org/10.13057/ijas.v3i2.42125.

Full text
Abstract:
<p>Nadaraya-Watson Estimator with kernel approach depends on two-parameter, those are kernel function and bandwidth choice. However, between the two of them, bandwidth choice gave a huge impact on the result of the estimation. By minimizing the value of Mean Square Error (MSE), Cross-Validation (CV) and Generalized Cross-Validation (GCV) gave the optimal bandwidth value. In this research, corn production was considered as the dependent variable, while the planted area, harvested area, and the fertilizer as the independent variable. The result of this research showed that Nadaraya-Watson Estimator with Generalized Cross-Validation gives a better corn production estimation with optimal bandwidth value 742392,2, with and with MSE 202583,9.</p><p><strong>Keywords</strong>: kernel, estimator Nadaraya-Watson, cross validation, generalized cross validation.</p>
APA, Harvard, Vancouver, ISO, and other styles
2

Rao, Nageswara S. V. "Nadaraya-Watson estimator for sensor fusion." Optical Engineering 36, no. 3 (March 1, 1997): 642. http://dx.doi.org/10.1117/1.601136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Chan, Nigel, and Qiying Wang. "UNIFORM CONVERGENCE FOR NONPARAMETRIC ESTIMATORS WITH NONSTATIONARY DATA." Econometric Theory 30, no. 5 (April 25, 2014): 1110–33. http://dx.doi.org/10.1017/s026646661400005x.

Full text
Abstract:
Sharp upper and lower uniform bounds are established for a general class of functionals of integrated and fractionally integrated time series. The main result is used to develop optimal uniform convergence for the Nadaraya-Watson estimator and the local linear nonparametric estimator in a nonlinear cointegrating regression model. Unlike the point-wise situation, it is shown that the performance of the local linear nonparametric estimator is superior to that of the Nadaraya-Watson estimator in uniform asymptotics.
APA, Harvard, Vancouver, ISO, and other styles
4

A. Hamad, Sarwar, and Kawa S. Mohamed Ali. "A Comparative Study of Nearest Neighbor Regression and Nadaraya Watson Regression." Academic Journal of Nawroz University 10, no. 2 (May 24, 2021): 180–88. http://dx.doi.org/10.25007/ajnu.v10n2a505.

Full text
Abstract:
Two non-parametric statistical methods are studied in this work. These are the nearest neighbor regression and the Nadaraya Watson kernel smoothing technique. We have proven that under a precise circumstance, the nearest neighborhood estimator and the Nadaraya Watson smoothing produce a smoothed data with a same error level, which means they have the same performance. Another result of the paper is that nearest neighborhood estimator performs better locally, but it graphically shows a weakness point when a large data set is considered on a global scale.
APA, Harvard, Vancouver, ISO, and other styles
5

Poměnková, Jitka. "Nonparametric estimate remarks." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 54, no. 3 (2006): 93–100. http://dx.doi.org/10.11118/actaun200654030093.

Full text
Abstract:
Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model. This article is focused on kernel smoothing for fixed design regresion model with three types of estimators, the Gasser-Müller estimator, the Nadaraya-Watson estimator and the local linear estimator. At the end of this article figures for ilustration of desribed estimators on simulated and real data sets are shown.
APA, Harvard, Vancouver, ISO, and other styles
6

Li, Jiexiang. "On asymptotic behavior of Nadaraya–Watson regression estimator." Communications in Statistics - Theory and Methods 45, no. 19 (July 19, 2016): 5751–61. http://dx.doi.org/10.1080/03610926.2014.948209.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hussein, Saja Mohammad. "Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation." Baghdad Science Journal 16, no. 4 (December 1, 2019): 0918. http://dx.doi.org/10.21123/bsj.2019.16.4.0918.

Full text
Abstract:
In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.
APA, Harvard, Vancouver, ISO, and other styles
8

Park, Cheolyong, and Tae Yoon Kim. "Bootstrapping stationary sequences by the Nadaraya-Watson regression estimator." Journal of Nonparametric Statistics 14, no. 4 (January 2002): 399–407. http://dx.doi.org/10.1080/10485250213116.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Khulood, Hamed Aljuhani, and Ismail Al turk Lutfiah. "Modification of the adaptive Nadaraya-Watson kernel regression estimator." Scientific Research and Essays 9, no. 22 (November 30, 2014): 966–71. http://dx.doi.org/10.5897/sre2014.6121.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Kim, Tae Yoon, Myung Sang Moon, and Sangyeol Lee. "Large bandwidth asymptotics for Nadaraya–Watson auto-regression estimator." Journal of the Korean Statistical Society 37, no. 4 (December 2008): 313–22. http://dx.doi.org/10.1016/j.jkss.2008.02.003.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Nadaraya-Watson estimator"

1

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.

Full text
Abstract:
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 class of nonparametric procedures by estimating unknown function as a weighted average using a kernel function. The bandwidth which determines the influence of the kernel has to be adapted to any kernel type estimator. Our focus is on Nadaraya-Watson estimator and Local Linear estimator which belong to a class of kernel type regression estimators called local polynomial kerne l estimators. A closely related problem is the determination of an appropriate sample size that would be required to achieve a desired confidence level of accuracy for the nonparametric regression estimators. Since sequential procedures allow an experimenter to make decisions based on the smallest number of observations without compromising accuracy, application of sequential procedures to a nonparametric regression model at a given point or series of points is considered. The motivation for using such procedures is: in many applications the quality of estimating an underlying regression function in a controlled experiment is paramount; thus, it is reasonable to invoke a sequential procedure of estimation that chooses a sample size based on recorded observations that guarantees a preassigned accuracy. We have employed sequential techniques to develop a procedure for constructing a fixed-width confidence interval for the predicted value at a specific point of the independent variable. These fixed-width confidence intervals are developed using asymptotic properties of both Nadaraya-Watson and local linear kernel estimators of nonparametric kernel regression with data-driven bandwidths and studied for both fixed and random design contexts. The sample sizes for a preset confidence coefficient are optimized using sequential procedures, namely two-stage procedure, modified two-stage procedure and purely sequential procedure. The proposed methodology is first tested by employing a large-scale simulation study. The performance of each kernel estimation method is assessed by comparing their coverage accuracy with corresponding preset confidence coefficients, proximity of computed sample sizes match up to optimal sample sizes and contrasting the estimated values obtained from the two nonparametric methods with act ual values at given series of design points of interest. We also employed the symmetric bootstrap method which is considered as an alternative method of estimating properties of unknown distributions. Resampling is done from a suitably estimated residual distribution and utilizes the percentiles of the approximate distribution to construct confidence intervals for the curve at a set of given design points. A methodology is developed for determining whether it is advantageous to use the symmetric bootstrap method to reduce the extent of oversampling that is normally known to plague Stein's two-stage sequential procedure. The procedure developed is validated using an extensive simulation study and we also explore the asymptotic properties of the relevant estimators. Finally, application of our proposed sequential nonparametric kernel regression methods are made to some problems in software reliability and finance.
APA, Harvard, Vancouver, ISO, and other styles
2

Reding, Lucas. "Contributions au théorème central limite et à l'estimation non paramétrique pour les champs de variables aléatoires dépendantes." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR049.

Full text
Abstract:
La thèse suivante traite du Théorème Central Limite pour des champs de variables aléatoires dépendantes et de son application à l’estimation non-paramétrique. Dans une première partie, nous établissons des théorèmes centraux limite quenched pour des champs satisfaisant une condition projective à la Hannan (1973). Les versions fonctionnelles de ces théorèmes sont également considérées. Dans une seconde partie, nous établissons la normalité asymptotique d’estimateurs à noyau de la densité et de la régression pour des champs fortement mélangeants au sens de Rosenblatt (1956) ou bien des champs faiblement dépendants au sens de Wu (2005). Dans un premier temps, nous établissons les résultats pour l’estimateur à noyau de la régression introduit par Elizbar Nadaraya (1964) et Geoffrey Watson (1964). Puis, dans un second temps, nous étendons ces résultats à une large classe d’estimateurs récursifs introduite par Peter Hall et Prakash Patil (1994)
This thesis deals with the central limit theorem for dependent random fields and its applications to nonparametric statistics. In the first part, we establish some quenched central limit theorems for random fields satisfying a projective condition à la Hannan (1973). Functional versions of these theorems are also considered. In the second part, we prove the asymptotic normality of kernel density and regression estimators for strongly mixing random fields in the sense of Rosenblatt (1956) and for weakly dependent random fields in the sense of Wu (2005). First, we establish the result for the kernel regression estimator introduced by Elizbar Nadaraya (1964) and Geoffrey Watson (1964). Then, we extend these results to a large class of recursive estimators defined by Peter Hall and Prakash Patil (1994)
APA, Harvard, Vancouver, ISO, and other styles
3

Joutard, Cyrille. "Grandes déviations en statistique asymptotique." Toulouse 3, 2004. http://www.theses.fr/2004TOU30071.

Full text
Abstract:
Dans cette thèse, nous étudions quelques problèmes de grandes déviations en statistique asymptotique. Dans une première partie, nous nous intéressons à des estimateurs non paramétriques. Des résultats de grandes déviations avaient été obtenus pour l'estimateur de Parzen-Rosenblatt de la densité et pour l'estimateur de Nadaraya-Watson de la régression. Nous complétons ces travaux en prouvant des principes de grandes déviations pour ces deux estimateurs sous des hypothèses plus générales. Puis nous donnons des résultats de grandes déviations précises. Dans une seconde partie, nous établissons un principe de grandes déviations général pour des M-estimateurs. Le cadre est celui d'un modèle paramétrique multidimensionnel où les observations ne sont pas nécessairement indépendantes et identiquement distribuées. Nous appliquons ensuite nos résultats à quelques modèles statistiques: modèles elliptiques, modèles exponentiels, modèles non linéaires, modèles linéaires généralisés. Enfin, dans une dernière partie, nous prouvons un principe de grandes déviations pour l'estimateur des moindres carrés, dans un modèle linéaire multidimensionnel. Nous utilisons ensuite ce résultat pour trouver des plans d'expériences optimaux. Le critère utilisé est l'efficacité asymptotique de Bahadur. Nous considérons d'abord un modèle linéaire gaussien. Puis, nous étudions le cas d'un modèle linéaire quelconque dont les erreurs satisfont certaines hypothèses.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Nadaraya-Watson estimator"

1

Ferraty, Frédéric, and Philippe Vieu. Kernel Regression Estimation for Functional Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.4.

Full text
Abstract:
This article provides an overview of recent nonparametric and semiparametric advances in kernel regression estimation for functional data. In particular, it considers the various statistical techniques based on kernel smoothing ideas that have recently been developed for functional regression estimation problems. The article first examines nonparametric functional regression modelling before discussing three popular functional regression estimates constructed by means of kernel ideas, namely: the Nadaraya-Watson convolution kernel estimate, the kNN functional estimate, and the local linear functional estimate. Uniform asymptotic results are then presented. The article proceeds by reviewing kernel methods in semiparametric functional regression such as single functional index regression and partial linear functional regression. It also looks at the use of kernels for additive functional regression and concludes by assessing the impact of kernel methods on practical real-data analysis involving functional (curves) datasets.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Nadaraya-Watson estimator"

1

Jiang, Jie, Yulin He, and Joshua Zhexue Huang. "Particle Swarm Optimization-Based Weighted-Nadaraya-Watson Estimator." In Lecture Notes in Computer Science, 267–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04503-6_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Qiao, Hongzhu, Nageswara S. V. Rao, and V. Protopopescuz. "PAC learning using Nadaraya-Watson estimator based on orthonormal systems." In Lecture Notes in Computer Science, 146–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63577-7_41.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Dudek, Grzegorz. "Tournament Searching Method for Optimization of the Forecasting Model Based on the Nadaraya-Watson Estimator." In Artificial Intelligence and Soft Computing, 339–48. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07176-3_30.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Yumin. "Bandwidth Selection for Nadaraya-Watson Kernel Estimator Using Cross-Validation Based on Different Penalty Functions." In Communications in Computer and Information Science, 88–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Walk, Harro. "Almost Sure Convergence Properties of Nadaraya-Watson Regression Estimates." In International Series in Operations Research & Management Science, 201–23. New York, NY: Springer US, 2002. http://dx.doi.org/10.1007/0-306-48102-2_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

"The Nadaraya–Watson kernel regression function estimator." In Topics in Advanced Econometrics, 212–47. Cambridge University Press, 1994. http://dx.doi.org/10.1017/cbo9780511599279.011.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Nadaraya-Watson estimator"

1

Dudek, Grzegorz, and Pawel Pelka. "Medium-term electric energy demand forecasting using Nadaraya-Watson estimator." In 2017 18th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 2017. http://dx.doi.org/10.1109/epe.2017.7967255.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Li-Yuan Xu, Min Zhang, Wei Zhu, and Yu-Lin He. "Comparison of geometric and arithmetic means for bandwidth selection in Nadaraya-Watson kernel regression estimator." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890742.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nunes, Flávio, and José Maia. "Continuous Monitoring in Wireless Sensor Networks: A Fuzzy-Probabilistic Approach." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/eniac.2019.9275.

Full text
Abstract:
This work presents and evaluates a fuzzy-probabilistic strategy to save energy in Wireless Sensor Networks (WSNs). The energy savings are obtained with the sensor nodes, no longer sensing and transmitting measurements. In this simple strategy, in each epoch each sensor node transmits its measurement with probability p, and does not transmit with probability (1 􀀀 p), does not correlate with that of any other sensor node. The task at the sink node, which is to estimate the sensor field at non-sensed points, is solved using fuzzy inference to impute the non-transmitted data followed by regression or interpolation of the sensed scalar field. In this, Nadaraya-Watson regression, regression with Fuzzy Inference and Radial Base Functions Interpolation are compared. The compromise curve between the value of p and the accuracy of the sensor field estimation measured by root mean square error (RMSE) is investigated. When compared to a published linear prediction strategy of the literature, the results show a small loss of performance versus the great simplification of the procedure in the sensor node, making it advantageous in applications that require extremely simple network nodes.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Nadaraya-Watson estimator"

1

Linton, Oliver, and Seok Young Hong. Asymptotic properties of a Nadaraya-Watson type estimator for regression functions of in finite order. The IFS, November 2016. http://dx.doi.org/10.1920/wp.cem.2016.5316.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Nadaraya-Watson estimator' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography