Academic literature on the topic 'Sequential nonparametric kernel regression'
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Journal articles on the topic "Sequential nonparametric kernel regression"
Zenkov, I. V., A. V. Lapko, V. A. Lapko, S. T. Im, V. P. Tuboltsev, and V. L. Аvdeenok. "A nonparametric algorithm for automatic classification of large multivariate statistical data sets and its application." Computer Optics 45, no. 2 (April 2021): 253–60. http://dx.doi.org/10.18287/2412-6179-co-801.
Full textMalik, Azeem, and Wing Lon Ng. "Intraday liquidity patterns in limit order books." Studies in Economics and Finance 31, no. 1 (February 25, 2014): 46–71. http://dx.doi.org/10.1108/sef-11-2011-0093.
Full textEichner, Gerrit, and Winfried Stute. "Kernel adjusted nonparametric regression." Journal of Statistical Planning and Inference 142, no. 9 (September 2012): 2537–44. http://dx.doi.org/10.1016/j.jspi.2012.03.011.
Full textGyorfi, Laszlo, Gabor Lugosi, and Frederic Udina. "NONPARAMETRIC KERNEL-BASED SEQUENTIAL INVESTMENT STRATEGIES." Mathematical Finance 16, no. 2 (April 2006): 337–57. http://dx.doi.org/10.1111/j.1467-9965.2006.00274.x.
Full textZhao, Ge, and Yanyuan Ma. "Robust nonparametric kernel regression estimator." Statistics & Probability Letters 116 (September 2016): 72–79. http://dx.doi.org/10.1016/j.spl.2016.04.010.
Full textHalconruy, H., and N. Marie. "Kernel Selection in Nonparametric Regression." Mathematical Methods of Statistics 29, no. 1 (January 2020): 32–56. http://dx.doi.org/10.3103/s1066530720010044.
Full textKyung-Joon, Cha, and William R. Schucany. "Nonparametric kernel regression estimation near endpoints." Journal of Statistical Planning and Inference 66, no. 2 (March 1998): 289–304. http://dx.doi.org/10.1016/s0378-3758(97)00082-7.
Full textSubramanian, Sundarraman. "Median regression using nonparametric kernel estimation." Journal of Nonparametric Statistics 14, no. 5 (January 2002): 583–605. http://dx.doi.org/10.1080/10485250213907.
Full textAndrews, Donald W. K. "Nonparametric Kernel Estimation for Semiparametric Models." Econometric Theory 11, no. 3 (June 1995): 560–86. http://dx.doi.org/10.1017/s0266466600009427.
Full textOkumura, Hidenori, and Kanta Naito. "Weighted kernel estimators in nonparametric binomial regression." Journal of Nonparametric Statistics 16, no. 1-2 (February 2004): 39–62. http://dx.doi.org/10.1080/10485250310001624828.
Full textDissertations / Theses on the topic "Sequential nonparametric kernel regression"
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 textSignorini, David F. "Practical aspects of kernel smoothing for binary regression and density estimation." Thesis, n.p, 1998. http://oro.open.ac.uk/19923/.
Full textWang, Sejong. "Three nonparametric specification tests for parametric regression models : the kernel estimation approach." Connect to resource, 1994. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261492759.
Full textEl, Ghouch Anouar. "Nonparametric statistical inference for dependent censored data." Université catholique de Louvain, 2007. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-09262007-123927/.
Full textKim, Byung-Jun. "Semiparametric and Nonparametric Methods for Complex Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99155.
Full textDoctor of Philosophy
A variety of complex data has broadened in many research fields such as epidemiology, genomics, and analytical chemistry with the development of science, technologies, and design scheme over the past few decades. For example, in epidemiology, the matched case-crossover study design is used to investigate the association between the clustered binary outcomes of disease and a measurement error in covariate within a certain period by stratifying subjects' conditions. In genomics, high-correlated and high-dimensional(HCHD) data are required to identify important genes and their interaction effect over diseases. In analytical chemistry, multiple time series data are generated to recognize the complex patterns among multiple classes. Due to the great diversity, we encounter three problems in analyzing the following three types of data: (1) matched case-crossover data, (2) HCHD data, and (3) Time-series data. We contribute to the development of statistical methods to deal with such complex data. First, under the matched study, we discuss an idea about hypothesis testing to effectively determine the association between observed factors and risk of interested disease. Because, in practice, we do not know the specific form of the association, it might be challenging to set a specific alternative hypothesis. By reflecting the reality, we consider the possibility that some observations are measured with errors. By considering these measurement errors, we develop a testing procedure under the matched case-crossover framework. This testing procedure has the flexibility to make inferences on various hypothesis settings. Second, we consider the data where the number of variables is very large compared to the sample size, and the variables are correlated to each other. In this case, our goal is to identify important variables for outcome among a large amount of the variables and build their network. For example, identifying few genes among whole genomics associated with diabetes can be used to develop biomarkers. By our proposed approach in the second project, we can identify differentially expressed and important genes and their network structure with consideration for the outcome. Lastly, we consider the scenario of changing patterns of interest over time with application to gas chromatography. We propose an efficient detection method to effectively distinguish the patterns of multi-level subjects in time-trend analysis. We suggest that our proposed method can give precious information on efficient search for the distinguishable patterns so as to reduce the burden of examining all observations in the data.
Maity, Arnab. "Efficient inference in general semiparametric regression models." [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3075.
Full textDoruska, 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.
Full textPh. D.
Chu, Chi-Yang. "Applied Nonparametric Density and Regression Estimation with Discrete Data| Plug-In Bandwidth Selection and Non-Geometric Kernel Functions." Thesis, The University of Alabama, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262364.
Full textBandwidth selection plays an important role in kernel density estimation. Least-squares cross-validation and plug-in methods are commonly used as bandwidth selectors for the continuous data setting. The former is a data-driven approach and the latter requires a priori assumptions about the unknown distribution of the data. A benefit from the plug-in method is its relatively quick computation and hence it is often used for preliminary analysis. However, we find that much less is known about the plug-in method in the discrete data setting and this motivates us to propose a plug-in bandwidth selector. A related issue is undersmoothing in kernel density estimation. Least-squares cross-validation is a popular bandwidth selector, but in many applied situations, it tends to select a relatively small bandwidth, or undersmooths. The literature suggests several methods to solve this problem, but most of them are the modifications of extant error criterions for continuous variables. Here we discuss this problem in the discrete data setting and propose non-geometric discrete kernel functions as a possible solution. This issue also occurs in kernel regression estimation. Our proposed bandwidth selector and kernel functions perform well in simulated and real data.
Edwards, Adam Michael. "Precision Aggregated Local Models." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102125.
Full textDoctor of Philosophy
Occasionally, when describing the relationship between two variables, it may be helpful to use a so-called ``non-parametric" regression that is agnostic to the function that connects them. Gaussian Processes (GPs) are a popular method of non-parametric regression used for their relative flexibility and interpretability, but they have the unfortunate drawback of being computationally infeasible for large data sets. Past work into solving the scaling issues for GPs has focused on ``divide and conquer" style schemes that spread the data out across multiple smaller GP models. While these model make GP methods much more accessible to large data sets they do so either at the expense of local predictive accuracy of global surface continuity. Precision Aggregated Local Models (PALM) is a novel divide and conquer method for GP models that is scalable for large data while maintaining local accuracy and a smooth global model. I demonstrate that PALM can be built quickly, and performs well predictively compared to other state of the art methods. This document also provides a sequential algorithm for selecting the location of each local model, and variations on the basic PALM methodology.
Song, Song. "Confidence bands in quantile regression and generalized dynamic semiparametric factor models." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2010. http://dx.doi.org/10.18452/16341.
Full textIn many applications it is necessary to know the stochastic fluctuation of the maximal deviations of the nonparametric quantile estimates, e.g. for various parametric models check. Uniform confidence bands are therefore constructed for nonparametric quantile estimates of regression functions. The first method is based on the strong approximations of the empirical process and extreme value theory. The strong uniform consistency rate is also established under general conditions. The second method is based on the bootstrap resampling method. It is proved that the bootstrap approximation provides a substantial improvement. The case of multidimensional and discrete regressor variables is dealt with using a partial linear model. A labor market analysis is provided to illustrate the method. High dimensional time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science, e.g. macroeconomics, meteorology, medicine and financial engineering. One of the common approach is to separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via dynamic factor analysis. We propose a two-step estimation procedure. At the first step, we detrend the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under the dependent scenario. At the second step, we obtain the detrended low dimensional stochastic process (stationary).
Books on the topic "Sequential nonparametric kernel regression"
Brockmann, Michael. Local bandwidth selection in nonparametric kernel regression. Aachen: Verlag Shaker, 1993.
Find full textRacine, Jean. A nonparametric variable kernel method for local adaptive smoothing of regression functions and associated response coefficients. Toronto, Ont: Dept. of Economics, York University, 1991.
Find full textFerraty, 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 textMathematical Statistics: Theory and Applications. Berlin, Germany: De Gruyter, 2020.
Find full textBook chapters on the topic "Sequential nonparametric kernel regression"
Opsomer, Jean D., and F. Jay Breidt. "Nonparametric Regression Using Kernel and Spline Methods." In International Encyclopedia of Statistical Science, 974–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_419.
Full textLinton, Oliver B., Enno Mammen, Xihong Lin, and Raymond J. Carroll. "Correlation and Marginal Longitudinal Kernel Nonparametric Regression." In Proceedings of the Second Seattle Symposium in Biostatistics, 23–33. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-9076-1_2.
Full textHan, Min, and Zhi-ping Liang. "Probability Density Estimation Based on Nonparametric Local Kernel Regression." In Advances in Neural Networks - ISNN 2010, 465–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13278-0_60.
Full textNadaraya, E. A. "Limiting Distributions of Deviations of Kernel-Type Density Estimators." In Nonparametric Estimation of Probability Densities and Regression Curves, 62–114. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2583-0_4.
Full textTang, Hengjin, and Sadaaki Miyamoto. "Semi-supervised Sequential Kernel Regression Models with Pairwise Constraints." In Modeling Decisions for Artificial Intelligence, 166–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41550-0_15.
Full textBabilua, Petre K., and Elizbar A. Nadaraya. "On Nonparametric Kernel-Type Estimate of the Bernoulli Regression Function." In Applications of Mathematics and Informatics in Natural Sciences and Engineering, 19–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56356-1_2.
Full textFaraway, Julian J. "Sequential Design for the Nonparametric Regression of Curves and Surfaces." In Computing Science and Statistics, 104–10. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2856-1_13.
Full textNadaraya, E. A. "Asymptotic Properties of Certain Measures of Deviation for Kernel-Type Nonparametric Estimators of Probability Densities." In Nonparametric Estimation of Probability Densities and Regression Curves, 18–41. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2583-0_2.
Full textEichner, Gerrit. "Kader—An R Package for Nonparametric Kernel Adjusted Density Estimation and Regression." In From Statistics to Mathematical Finance, 291–315. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50986-0_15.
Full textSun, Huaiyu, Mi Zhu, and Feng He. "Pattern Recognition Based on the Nonparametric Kernel Regression Method in A-share Market." In Proceedings of International Conference on Soft Computing Techniques and Engineering Application, 309–14. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1695-7_35.
Full textConference papers on the topic "Sequential nonparametric kernel regression"
Zhang, Dongling, Yingjie Tian, and Peng Zhang. "Kernel-Based Nonparametric Regression Method." In 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2008. http://dx.doi.org/10.1109/wiiat.2008.157.
Full textRafajlowicz, Ewaryst, Miroslaw Pawlak, and Ansgar Steland. "Nonparametric sequential change-point detection by a vertical regression method." In 2009 IEEE/SP 15th Workshop on Statistical Signal Processing (SSP). IEEE, 2009. http://dx.doi.org/10.1109/ssp.2009.5278502.
Full textRismal, I. Nyoman Budiantara, and Dedy Dwi Prastyo. "Mixture model of spline truncated and kernel in multivariable nonparametric regression." In INNOVATIONS THROUGH MATHEMATICAL AND STATISTICAL RESEARCH: Proceedings of the 2nd International Conference on Mathematical Sciences and Statistics (ICMSS2016). Author(s), 2016. http://dx.doi.org/10.1063/1.4952565.
Full textMaulidia, Miftahul Jannah, I. Nyoman Budiantara, and Jerry D. T. Purnomo. "Nonparametric regression curve estimation using mixed spline truncated and kernel estimator for longitudinal data." In THE 2ND INTERNATIONAL CONFERENCE ON SCIENCE, MATHEMATICS, ENVIRONMENT, AND EDUCATION. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5139795.
Full textWang, Zhigang, Yong Zeng, and Ping Li. "A Nonparametric Kernel Regression Method for the Recognition of Visual Technical Patterns in China's Stock Market." In 2010 3rd International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2010. http://dx.doi.org/10.1109/bife.2010.76.
Full textNguyen, Vu, Dinh Phung, Trung Le, and Hung Bui. "Discriminative Bayesian Nonparametric Clustering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/355.
Full textReports on the topic "Sequential nonparametric kernel regression"
Haerdle, Wolfgang. Nonparametric Sequential Estimation of Zeros and Extrema of Regression Functions. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada169958.
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