Academic literature on the topic 'Generalised smoothing'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Generalised smoothing.'
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 "Generalised smoothing"
Pizarro, Luis, Pavel Mrázek, Stephan Didas, Sven Grewenig, and Joachim Weickert. "Generalised Nonlocal Image Smoothing." International Journal of Computer Vision 90, no. 1 (April 9, 2010): 62–87. http://dx.doi.org/10.1007/s11263-010-0337-7.
Full textOsborne, M. R., and Tania Prvan. "On algorithms for generalised smoothing splines." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 29, no. 3 (January 1988): 322–41. http://dx.doi.org/10.1017/s0334270000005841.
Full textOsborne, M. R., and Tania Prvan. "Smoothness and conditioning in generalised smoothing spline calculations." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 30, no. 1 (July 1988): 43–56. http://dx.doi.org/10.1017/s0334270000006020.
Full textFriston, Karl, Klaas Stephan, Baojuan Li, and Jean Daunizeau. "Generalised Filtering." Mathematical Problems in Engineering 2010 (2010): 1–34. http://dx.doi.org/10.1155/2010/621670.
Full textLu, Yiqiang, and Riquan Zhang. "Smoothing spline estimation of generalised varying-coefficient mixed model." Journal of Nonparametric Statistics 21, no. 7 (October 2009): 815–25. http://dx.doi.org/10.1080/10485250903151078.
Full textChaubey, Yogendra P., Naâmane Laïb, and Arusharka Sen. "Generalised kernel smoothing for non-negative stationary ergodic processes." Journal of Nonparametric Statistics 22, no. 8 (November 2010): 973–97. http://dx.doi.org/10.1080/10485251003605120.
Full textTroudi, Molka, and Faouzi Ghorbel. "The Generalised Plug-in Algorithm for the Diffeomorphism Kernel Estimate." International Journal of Mathematics and Computers in Simulation 15 (November 27, 2021): 128–33. http://dx.doi.org/10.46300/9102.2021.15.24.
Full textLiu, Yaping. "Smoothing the domain out for positive solutions." Bulletin of the Australian Mathematical Society 61, no. 3 (June 2000): 405–13. http://dx.doi.org/10.1017/s0004972700022437.
Full textHancock, P. A., and M. F. Hutchinson. "An iterative procedure for calculating minimum generalised cross validation smoothing splines." ANZIAM Journal 44 (April 1, 2003): 290. http://dx.doi.org/10.21914/anziamj.v44i0.683.
Full textOmbao, H. C., J. A. Raz, R. L. Strawderman, and R. Von Sachs. "A simple generalised crossvalidation method of span selection for periodogram smoothing." Biometrika 88, no. 4 (December 1, 2001): 1186–92. http://dx.doi.org/10.1093/biomet/88.4.1186.
Full textDissertations / Theses on the topic "Generalised smoothing"
Li, Yuyi. "Empirical likelihood with applications in time series." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/empirical-likelihood-with-applications-in-time-series(29c74808-f784-4306-8df9-26f45b30b553).html.
Full textSheppard, Therese. "Extending covariance structure analysis for multivariate and functional data." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/extending-covariance-structure-analysis-for-multivariate-and-functional-data(e2ad7f12-3783-48cf-b83c-0ca26ef77633).html.
Full textBaker, Jannah F. "Bayesian spatiotemporal modelling of chronic disease outcomes." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104455/1/Jannah_Baker_Thesis.pdf.
Full textUtami, Zuliana Sri. "Penalized regression methods with application to generalized linear models, generalized additive models, and smoothing." Thesis, University of Essex, 2017. http://repository.essex.ac.uk/20908/.
Full textWang, Xiaohui 1969. "Combining the generalized linear model and spline smoothing to analyze examination data." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=26176.
Full textThe statistical tools used in this thesis are the generalized linear models and spline smoothing. The method tries to combine the advantages of both parametric modeling and nonparametric regression to get a good estimate of the item characteristic curve. A special basis for spline smoothing is proposed which is motivated by the properties of the item characteristic curve. Based on the estimate of the item characteristic curve by this method, a more stable estimate of the item information function can be generated. Some illustrative analysis of simulated data are presented. The results seem to indicate that this method does have the advantages of both parametric modeling and nonparametric regression: it is faster to compute and more flexible than the methods using parametric models, for example, the three-parameter model in psychometrics, and on the other hand, it generates more stable estimate of derivatives than the purely nonparametric regression.
Cao, Jiguo. "Generalized profiling method and the applications to adaptive penalized smoothing, generalized semiparametric additive models and estimating differential equations." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102483.
Full textFirst, penalized smoothing is extended by allowing for a functional smoothing parameter, which is adaptive to the geometry of the underlying curve, which is called adaptive penalized smoothing. In the first level of optimization, the smooth ing coefficients are local parameters, estimated by minimizing sum of squared errors, conditional on the functional smoothing parameter. In the second level, the functional smoothing parameter is a complexity parameter, estimated by minimizing generalized cross-validation (GCV), treating the smoothing coefficients as explicit functions of the functional smoothing parameter. Adaptive penalized smoothing is shown to obtain better estimates for fitting functions and their derivatives.
Next, the generalized semiparametric additive models are estimated by three levels of optimization, allowing response variables in any kind of distribution. In the first level, the nonparametric functional parameters are nuisance parameters, estimated by maximizing the regularized likelihood function, conditional on the linear coefficients and the smoothing parameter. In the second level, the linear coefficients are structural parameters, estimated by maximizing the likelihood function with the nonparametric functional parameters treated as implicit functions of linear coefficients and the smoothing parameter. In the third level, the smoothing parameter is a complexity parameter, estimated by minimizing the approximated GCV with the linear coefficients treated as implicit functions of the smoothing parameter. This method is applied to estimate the generalized semiparametric additive model for the effect of air pollution on the public health.
Finally, parameters in differential equations (DE's) are estimated from noisy data with the generalized profiling method. In the first level of optimization, fitting functions are estimated to approximate DE solutions by penalized smoothing with the penalty term defined by DE's, fixing values of DE parameters. In the second level of optimization, DE parameters are estimated by weighted sum of squared errors, with the smoothing coefficients treated as an implicit function of DE parameters. The effects of the smoothing parameter on DE parameter estimates are explored and the optimization criteria for smoothing parameter selection are discussed. The method is applied to fit the predator-prey dynamic model to biological data, to estimate DE parameters in the HIV dynamic model from clinical trials, and to explore dynamic models for thermal decomposition of alpha-Pinene.
Kaivanipour, Kivan. "Non-Life Insurance Pricing Using the Generalized Additive Model, Smoothing Splines and L-Curves." Thesis, KTH, Matematik (Avd.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168389.
Full textNästan alla tariffanalyser inom sakförsäkring inkluderar kontinuerliga premieargument, såsom försäkringstagarens ålder eller vikten på det försäkrade fordonet. I den generaliserade linjära modellen så grupperas kontinuerliga premiearguments möjliga värden i intervaller och alla värden inom ett intervall behandlas som identiska. Genom att använda den generaliserade additativa modellen så slipper man arbetet med att dela in kontinuerliga premiearguments möjliga värden i intervaller. Detta examensarbete kommer att behandla olika metoder för att uppskatta den optimala smoothing-parametern inom den generaliserade additativa modellen. Metoden för korsvalidering används vanligen för detta ändamål. L-kurve-metoden, som är en mer ovanlig metod, undersöks för dess prestanda i jämförelse med metoden för korsvalidering. Numeriska beräkningar på testdata visar att L-kurve-metoden är betydligt snabbare än metoden för korsvalidering, men att den underutjämnar och därför inte är en lämplig metod för att uppskatta den optimala smoothing-parametern.
Pya, Natalya. "Additive models with shape constraints." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527433.
Full textBjörkwall, Susanna. "Stochastic claims reserving in non-life insurance : Bootstrap and smoothing models." Doctoral thesis, Stockholms universitet, Matematiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-55347.
Full textHanh, Nguyen T. "Lasso for Autoregressive and Moving Average Coeffients via Residuals of Unobservable Time Series." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo154471227291601.
Full textBooks on the topic "Generalised smoothing"
Franke, Richard H. Laplacian smoothing splines with generalized cross validation for objective analysis of meteorological data. Monterey, California: Naval Postgraduate School, 1985.
Find full textHastie, Trevor. Generalized additive models. Toronto: University of Toronto, Dept. of Statistics, 1985.
Find full textHastie, Trevor. Generalized additive models. Boca Raton, Fla: Chapman & Hall/CRC, 1999.
Find full textHastie, Trevor. Generalized additive models. London: Chapman and Hall, 1990.
Find full textHastie, Trevor. Generalized additive models: Some applications. Toronto: University of Toronto, Dept. of Statistics, 1985.
Find full textBook chapters on the topic "Generalised smoothing"
Schimek, Michael G., and Berwin A. Turlach. "Additive and Generalized Additive Models." In Smoothing and Regression, 277–327. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118150658.ch10.
Full textGreen, Peter J., and Bernard W. Silverman. "Interpolating and smoothing splines." In Nonparametric Regression and Generalized Linear Models, 11–27. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-4473-3_2.
Full textConversano, Claudio. "Smoothing Score Algorithm for Generalized Additive Models." In Studies in Classification, Data Analysis, and Knowledge Organization, 95–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_8.
Full textKmit, I. "Smoothing Effect and Fredholm Property for First-order Hyperbolic PDEs." In Pseudo-Differential Operators, Generalized Functions and Asymptotics, 219–38. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0585-8_11.
Full textFahrmeir, Ludwig, Wolfgang Hennevogl, and Karola Klemme. "Smoothing in dynamic generalized linear models by Gibbs sampling." In Advances in GLIM and Statistical Modelling, 85–90. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2952-0_14.
Full textTrinh, Philippe H. "Exponential Asymptotics and Stokes Line Smoothing for Generalized Solitary Waves." In Asymptotic Methods in Fluid Mechanics: Survey and Recent Advances, 121–26. Vienna: Springer Vienna, 2010. http://dx.doi.org/10.1007/978-3-7091-0408-8_4.
Full textRinott, Yosef, and Natalie Shlomo. "A Generalized Negative Binomial Smoothing Model for Sample Disclosure Risk Estimation." In Privacy in Statistical Databases, 82–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11930242_8.
Full textSaika, Yohei, Kouki Sugimoto, and Ken Okamoto. "Performance Estimation of Generalized Statistical Smoothing to Inverse Halftoning Based on the MTF Function of Human Eyes." In Algorithms and Architectures for Parallel Processing, 358–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13136-3_37.
Full textHastie, T. J., and R. J. Tibshirani. "Smoothing." In Generalized Additive Models, 9–38. Routledge, 2017. http://dx.doi.org/10.1201/9780203753781-2.
Full text"Generalized Smoothing Spline ANOVA." In Smoothing Splines. Chapman and Hall/CRC, 2011. http://dx.doi.org/10.1201/b10954-7.
Full textConference papers on the topic "Generalised smoothing"
Kpalma, K., and J. Ronsin. "A multi-scale curve smoothing for generalised pattern recognition (MSGPR)." In Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isspa.2003.1224905.
Full textWilkin, Tim, and Gleb Beliakov. "Robust image denoising and smoothing with generalised spatial-tonal averages." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015433.
Full textWhite, Langford B., and Francesco Carravetta. "Stochastic realisation and optimal smoothing for Gaussian generalised reciprocal processes." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263692.
Full textRandell, David, Yanyun Wu, Philip Jonathan, and Kevin Ewans. "Modelling Covariate Effects in Extremes of Storm Severity on the Australian North West Shelf." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10187.
Full textMattern, Christopher. "Generalized Probability Smoothing." In 2018 Data Compression Conference (DCC). IEEE, 2018. http://dx.doi.org/10.1109/dcc.2018.00033.
Full textNiedzwiecki, Maciej, and Adam Sobocinski. "Generalized adaptive notch smoothing algorithms." In European Control Conference 2007 (ECC). IEEE, 2007. http://dx.doi.org/10.23919/ecc.2007.7068700.
Full textBittanti, Sergio, and Francesco Alessandro Cuzzola. "Robust generalized H2 smoothing with unbiasedness." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076229.
Full textChen, Jer-Sen. "Generalized adaptive smoothing for multiscale edge detection." In Aerospace Sensing, edited by Kevin W. Bowyer. SPIE, 1992. http://dx.doi.org/10.1117/12.58584.
Full textAtzmon, Yuval, and Gal Chechik. "Adaptive Confidence Smoothing for Generalized Zero-Shot Learning." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01194.
Full textFang, Haian, and Joseph H. Nurre. "Smoothing head scan data with generalized cross validation." In Optical Tools for Manufacturing and Advanced Automation, edited by Sabry F. El-Hakim. SPIE, 1993. http://dx.doi.org/10.1117/12.162128.
Full text