Bibliographies thématiques / Nonparametrica

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Littérature scientifique sur le sujet « Nonparametrica »

Auteur : Grafiati
Publié le 9 mars 2023
Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Nonparametrica"

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Firpha, Rosse Millania Pichago, and Anneke Iswani Achmad. "Regresi Nonparametrik Spline Truncated untuk Pemodelan Persentase Penduduk Miskin di Jawa Barat Pada Tahun 2021." Bandung Conference Series: Statistics 2, no. 2 (2022): 454–58. http://dx.doi.org/10.29313/bcss.v2i2.4720.

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Abstract. Nonparametric regression is a statistical method used to model the relationship between response variables and predictor variables whose pattern shape is unknown. In nonparametric regression there are several approaches, one of which is the spline. Spline nonparametric regression, if there is one predictor variable then the regression is called univariable spline nonparametric regression. Conversely, if there is one response variable with more than one predictor variable then the regression is called a multivariable spline nonparametric regression. In nonparametric regression there i
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Hallam, Arne. "A Brief Overview of Nonparametric Methods in Economics." Northeastern Journal of Agricultural and Resource Economics 21, no. 2 (1992): 98–112. http://dx.doi.org/10.1017/s0899367x00002610.

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The concept of nonparametric analysis, estimation, and inference has a long and storied existence in the annals of economic measurement. At least four rather distinct types of analysis are lumped under the broad heading of nonparametrics. The oldest, and perhaps most common, is that associated with distribution-free methods and order statistics. Similar in spirit, but different in emphasis, is nonparametric density estimation, such as the currently popular kernel estimator for regression. Semi-parametric or semi-nonparametric estimation combines parametric analysis of portions of the problem w
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Dani, Andrea Tri Rian, and Narita Yuri Adrianingsih. "Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier." Jambura Journal of Mathematics 1, no. 1 (2021): 26–36. http://dx.doi.org/10.34312/jjom.v1i1.7713.

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ABSTRAKPendekatan regresi nonparametrik digunakan apabila hubungan antara variabel prediktor dan variabel respon tidak diketahui polanya. Spline truncated dan deret Fourier merupakan estimator dalam pendekatan nonparametrik yang terkenal, karena memiliki fleksibilitas yang tinggi dan mampu menyesuaikan terhadap sifat lokal data secara efektif. Penelitian ini bertujuan untuk mendapatkan estimator model regresi nonparametrik terbaik menggunakan spline truncated dan deret Fourier. Metode estimasi kurva regresi nonparametrik dilakukan dengan menyelesaikan optimasi Ordinary Least Squares (OLS). Kri
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Dani, Andrea Tri Rian, and Narita Yuri Adrianingsih. "Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier." Jambura Journal of Mathematics 3, no. 1 (2021): 26–36. http://dx.doi.org/10.34312/jjom.v3i1.7713.

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ABSTRAKPendekatan regresi nonparametrik digunakan apabila hubungan antara variabel prediktor dan variabel respon tidak diketahui polanya. Spline truncated dan deret Fourier merupakan estimator dalam pendekatan nonparametrik yang terkenal, karena memiliki fleksibilitas yang tinggi dan mampu menyesuaikan terhadap sifat lokal data secara efektif. Penelitian ini bertujuan untuk mendapatkan estimator model regresi nonparametrik terbaik menggunakan spline truncated dan deret Fourier. Metode estimasi kurva regresi nonparametrik dilakukan dengan menyelesaikan optimasi Ordinary Least Squares (OLS). Kri
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Budiantara, I. Nyoman. "APLIKASI SPLINE ESTIMATOR TERBOBOT." Jurnal Teknik Industri 3, no. 2 (2004): 57–62. http://dx.doi.org/10.9744/jti.3.2.57-62.

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We considered the nonparametric regression model : Zj = X(tj) + ej, j = 1,2, ,n, where X(tj) is the regression curve. The random error ej are independently distributed normal with a zero mean and a variance s2/bj, bj > 0. The estimation of X obtained by minimizing a Weighted Least Square. The solution of this optimation is a Weighted Spline Polynomial. Further, we give an application of weigted spline estimator in nonparametric regression.
 
 
 Abstract in Bahasa Indonesia : 
 
 Diberikan model regresi nonparametrik : Zj = X(tj) + ej, j = 1,2, ,n, dengan X (tj) kurv
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Lestari, Budi. "Estimasi Fungsi Regresi Dalam Model Regresi Nonparametrik Birespon Menggunakan Estimator Smoothing Spline dan Estimator Kernel." Jurnal Matematika Statistika dan Komputasi 15, no. 2 (2018): 20. http://dx.doi.org/10.20956/jmsk.v15i2.5710.

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Abstract Regression model of bi-respond nonparametric is a regression model which is illustrating of the connection pattern between respond variable and one or more predictor variables, where between first respond and second respond have correlation each other. In this paper, we discuss the estimating functions of regression in regression model of bi-respond nonparametric by using different two estimation techniques, namely, smoothing spline and kernel. This study showed that for using smoothing spline and kernel, the estimator function of regression which has been obtained in observation is a
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Safni Chusnaifah Junianingsih. "Regresi Nonparametrik Kernel dalam Pemodelan Jumlah Kelahiran Bayi di Jawa Barat Tahun 2017." Bandung Conference Series: Statistics 1, no. 1 (2021): 30–37. http://dx.doi.org/10.29313/bcss.v1i1.39.

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Abstract. Regression analysis is one of the analytical tools used to determine the effect of multiple predictor variables (X) on response variables (Y). The approach in regression analysis is divided into two, parametric approaches and nonparametric approaches. On nonparametric regression analysis, the shape of the regression curve is unknown, the data arega expected to look for its own estimation form so that it has high flexibility. Estimation of regression functions is performed with the Nadaraya Watson kernel estimator using Gaussian kernel functions. In this method requires bandwidth (h)
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Karimuse, William Yulius, Darnah Andi Nohe, and Meiliyani Siringoringo. "Pendekatan Regresi Nonparametrik Kernel pada Data IHSG Periode Januari 2020 – Desember 2021." STATISTIKA Journal of Theoretical Statistics and Its Applications 23, no. 1 (2023): 1–7. http://dx.doi.org/10.29313/statistika.v23i1.1628.

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ABSTRAK
 Pendekatan regresi nonparametrik Kernel digunakan untuk memperkirakan harapan bersyarat dari variabel dependen terhadap variabel independen tanpa mengasumsikan bentuk parametrik tertentu. Pendekatan ini menggunakan fungsi Kernel sebagai alat untuk melakukan estimasi. Dalam penelitian ini, digunakan fungsi Kernel Gaussian dan estimator Nadaraya-Watson. Estimator Nadaraya-Watson adalah metode yang mengestimasi fungsi regresi sebagai rata-rata tertimbang secara lokal, dengan menggunakan fungsi Kernel sebagai pembobot. Pendekatan regresi nonparametrik Kernel ini juga efektif dalam me
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Dani, Andrea Tri Rian, Narita Yuri Adrianingsih, Alifta Ainurrochmah, and Riry Sriningsih. "Flexibility of Nonparametric Regression Spline Truncated on Data without a Specific Pattern." Jurnal Litbang Edusaintech 2, no. 1 (2021): 37–43. http://dx.doi.org/10.51402/jle.v2i1.30.

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Bentuk pola hubungan antara variabel prediktor dan variabel respon ada yang diketahui, namun pada nyatanya ada pula yang tidak diketahui. Apabila bentuk pola hubungan antara variabel respon dan variabel prediktor tidak diketahui, pendekatan regresi nonparametrik merupakan pendekatan yang paling sesuai. Pendekatan regresi nonparametrik tidak tergantung pada asumsi bentuk kurva regresi tertentu, sehingga akan memberikan fleksibilitas yang tinggi. Salah satu estimator regresi nonparametrik yang terkenal adalah spline truncated. Spline truncated merupakan potongan-potongan polinomial yang memiliki
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Izumi, Sukma Wiyasih, and Teti Sofia Yanti. "Pemodelan Tingkat Pengangguran Terbuka (TPT) di Jawa Barat Menggunakan Regresi Nonparametrik Deret Fourier." Bandung Conference Series: Statistics 3, no. 2 (2023): 594–601. http://dx.doi.org/10.29313/bcss.v3i2.8769.

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Abstract. Regression analysis is a method used to determine the causal relationship of response variables with one or more predictor variables. It is generally divided into three regression analyses namely parametric, semiparametric, and nonparametric. Parametric regression has assumptions that can be met and the regression curve is known, but not all data can meet this. Therefore, the use of nonparametric regression can be an alternative because the use is not bound by assumptions and is used when the data does not follow a certain curve pattern. Nonparametric regression of the Fourier series
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Thèses sur le sujet "Nonparametrica"

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CORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.

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I modelli mistura in ambito Bayesiano nonparametrico sono modelli flessibili per stime di densità e clustering, ormai uno strumento di uso comune in ambito statistico applicato. Il primo modello introdotto in questo ambito è stato il processo di Dirichlet (DP) (Ferguson, 1973) combinato con un kernel Gaussiano(Lo, 1984). Recentemente è cresciuto l’interesse verso la definizione di modelli mistura basati su misure nonparametriche che generalizzano il DP. Tra le misure proposte, il processo di Pitman-Yor (PY) (Perman et al., 1992; Pitman, 1995) e, più in generale, la classe di Gibbs-type prior (
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Campbell, Trevor D. J. (Trevor David Jan). "Truncated Bayesian nonparametrics." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107047.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 167-175).<br>Many datasets can be thought of as expressing a collection of underlying traits with unknown cardinality. Moreover, these datasets are often persistently growing, and we expect the number of expressed traits to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allow
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Li, Jiexiang. "Nonparametric spatial estimation." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3223036.

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Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2006.<br>"Title from dissertation home page (viewed June 28, 2007)." Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3167. Adviser: Lanh Tat Tran.
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Straub, Julian Ph D. Massachusetts Institute of Technology. "Nonparametric directional perception." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112029.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 239-257).<br>Artificial perception systems, like autonomous cars and augmented reality headsets, rely on dense 3D sensing technology such as RGB-D cameras and LiDAR. scanners. Due to the structural simplicity of man-made environments, understanding and leveraging not only the 3D data but also the local orientations of the constituent surfaces, has huge potential. From an indoor scene to lar
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Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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Rangel, Ruiz Ricardo. "Nonparametric and semi-nonparametric approaches to the demand for liquid assets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ64924.pdf.

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Yuan, Lin. "Bayesian nonparametric survival analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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Bush, Helen Meyers. "Nonparametric multivariate quality control." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/25571.

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Pedroso, Estevam de Souza Camila. "Switching nonparametric regression models." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/45130.

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In this thesis, we propose a methodology to analyze data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z, also called a hidden or latent state. The states form a stochastic process and their possible values are j=1,...,J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. In a Bayesian switching nonparametric regression model the uncertainty a
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Gosling, John Paul. "Elicitation : a nonparametric view." Thesis, University of Sheffield, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425613.

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Livres sur le sujet "Nonparametrica"

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Hjort, Nils Lid, Chris Holmes, Peter Muller, and Stephen G. Walker, eds. Bayesian Nonparametrics. Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511802478.

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Lid, Hjort Nils, ed. Bayesian nonparametrics. Cambridge University Press, 2009.

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Gibbons, Jean. Nonparametric Statistics. SAGE Publications, Inc., 1993. http://dx.doi.org/10.4135/9781412985314.

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Bertail, Patrice, Delphine Blanke, Pierre-André Cornillon, and Eric Matzner-Løber, eds. Nonparametric Statistics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96941-1.

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Cao, Ricardo, Wenceslao González Manteiga, and Juan Romo, eds. Nonparametric Statistics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41582-6.

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La Rocca, Michele, Brunero Liseo, and Luigi Salmaso, eds. Nonparametric Statistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57306-5.

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Klemelä, Jussi. Nonparametric Finance. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119409137.

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Pagan, A. R. Nonparametric Econometrics. Cambridge University Press, 1999.

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Aman, Ullah, ed. Nonparametric econometrics. Cambridge University Press, 1999.

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Sutor, David C. Nonparametric statistics. Addison-Wesley, 1993.

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Chapitres de livres sur le sujet "Nonparametrica"

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Heiberger, Richard M., and Burt Holland. "Nonparametrics." In Statistical Analysis and Data Display. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_16.

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Heiberger, Richard M., and Burt Holland. "Nonparametrics." In Statistical Analysis and Data Display. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_16.

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Pardo, Scott A. "Nonparametrics." In Statistical Methods and Analyses for Medical Devices. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26139-8_12.

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Chow, Shein-Chung, Jun Shao, Hansheng Wang, and Yuliya Lokhnygina. "Nonparametrics." In Sample Size Calculations in Clinical Research: Third Edition. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315183084-14.

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Lee, Myoung-jae. "Semi-Nonparametrics." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_10.

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Walker, Stephen Graham. "Bayesian Nonparametrics." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2596-1.

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Clarke, Bertrand, Ernest Fokoué, and Hao Helen Zhang. "Alternative Nonparametrics." In Principles and Theory for Data Mining and Machine Learning. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-98135-2_6.

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Sun, Jianguo, and Xingqiu Zhao. "Nonparametric Estimation." In Statistics for Biology and Health. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8715-9_3.

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Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. "Nonparametric Regression." In Regression. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34333-9_8.

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Kutoyants, Yu A. "Nonparametric Estimation." In Statistical Inference for Spatial Poisson Processes. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1706-0_7.

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Actes de conférences sur le sujet "Nonparametrica"

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Gonzalez, Jaime, Johannes Milz, and Eunhye Song. "Nonparametric Input-Output Uncertainty Comparisons." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838813.

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Enright, M. P., R. C. McClung, S. J. Hudak, and H. R. Millwater. "Application of Nonparametric Methods to Rainflow Stress Density Estimation of Gas Turbine Engine Usage." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90780.

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The risk of fracture associated with high energy rotating components in aircraft gas turbine engines can be sensitive to small changes in applied stress values which are often difficult to measure and predict. Although a parametric approach is often used to characterize random variables, it is difficult to apply to multimodal densities. Nonparametric methods provide a direct fit to the data, and can be used to estimate the multimodal densities often associated with rainflow stress data. In this paper, a comparison of parametric and nonparametric methods is presented for density estimation of r
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Tsang, Coates, and Nowak. "Nonparametric Internet tomography." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1006175.

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Tsang, Yolanda, Mark Coates, and Robert Nowak. "Nonparametric internet tomography." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745035.

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Mattar, Marwan A., Michael G. Ross, and Erik G. Learned-Miller. "Nonparametric curve alignment." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960369.

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PARK, BYEONG U. "NONPARAMETRIC ADDITIVE REGRESSION." In International Congress of Mathematicians 2018. WORLD SCIENTIFIC, 2019. http://dx.doi.org/10.1142/9789813272880_0169.

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Duan, Ling-Yu, Min Xu, Qi Tian, and Chang-Sheng Xu. "Nonparametric motion model." In the 12th annual ACM international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1027527.1027700.

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Yazhou Liu, Hongxun Yao, Wen Gao, Xilin Chen, and Debin Zhao. "Nonparametric Background Generation." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.868.

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Hague, Stephen, and Simaan AbouRizk. "Nonparametric frequency polygon estimation for modeling input data." In The 19th International Conference on Modelling and Applied Simulation. CAL-TEK srl, 2019. http://dx.doi.org/10.46354/i3m.2019.mas.020.

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To construct valid probability distributions solely from input data, this paper compares three nonparametric density estimators: (1) histograms, (2) Kernel Density Estimation, and (3) Frequency Polygon Estimation. A pseudocode is implemented, a practical example is illustrated, and the Simphony.NET simulation environment is used to fit the nonparametric frequency polygon to a set of data to recreate it as a posterior distribution via the Metropolis-Hastings algorithm.
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Liu, Si-Yu, Zao-Jian Zou, and Lu Zou. "Identification Modeling of Ship Maneuvering Motion Based on Echo State Gaussian Process." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-123756.

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Abstract The mathematical model of ship maneuvering motion is a crucial foundation for both prediction of ship navigation state and automatic control of ship motion. This paper proposes a practical and robust nonparametric identification method based on echo state Gaussian process (ESGP) to establish the nonparametric model of ship maneuvering motion. The traditional echo state networks (ESNs) and basic Gaussian processes (GPs) have found successfully applications in nonparametric modeling and time-series forecasting. The proposed method combines the strengths of both ESN and GP approaches. On
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Rapports d'organisations sur le sujet "Nonparametrica"

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Rodríguez, Diego J., and William J. Vaughan. A Note on Obtaining Welfare Bounds in Referendum Contingent Valuation Studies. Inter-American Development Bank, 2000. http://dx.doi.org/10.18235/0008802.

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In this note, the authors detail the mechanics of the nonparametric mean and variance calculations of finding willingness to pay (WTP) estimates. There are (at least) three nonparametric estimators of mean WTP that can be obtained from referendum CV survey data. The logic behind all three nonparametric estimators is the same.
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Owen, Arthur B. Nonparametric Conditional Estimation. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1454025.

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Owen, Arthur B. Nonparametric Conditional Estimation. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada590998.

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Horowitz, Joel L. Nonparametric additive models. Institute for Fiscal Studies, 2012. http://dx.doi.org/10.1920/wp.cem.2012.2012.

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Kim, Dongwoo, Denis Chetverikov, and Daniel Wilhelm. Nonparametric instrumental variable estimation. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.4717.

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Owen, Art. Nonparametric Likelihood Ratio Intervals. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162978.

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Dew-Becker, Ian, and Charles Nathanson. Directed Attention and Nonparametric Learning. National Bureau of Economic Research, 2017. http://dx.doi.org/10.3386/w23917.

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Chamberlain, Gary, and Guido Imbens. Nonparametric Applications of Bayesian Inference. National Bureau of Economic Research, 1996. http://dx.doi.org/10.3386/t0200.

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Andersen, Torben, Tim Bollerslev, and Francis Diebold. Parametric and Nonparametric Volatility Measurement. National Bureau of Economic Research, 2002. http://dx.doi.org/10.3386/t0279.

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Block, Henry W., and Thomas H. Savits. Multivariate Nonparametric Classes in Reliability. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada185645.

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