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Non-parametric identification of generalized Hammerstein models
International Journal of Systems Science, 1998The Hammerstein model is considered in a generalized form, where its nonlinear element can have multi-inputs and a finite memory. The identification of the multi-input finite memory nonimearity and the impulse response sequence of the model is treated using a non-parametric approach. A numerical example is given.
Hosam E. Emara-Shabaik +1 more
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Non-parametric Spectral Model for Shape Retrieval
2015 International Conference on 3D Vision, 2015Non-rigid 3D shape retrieval is an active and important research topic in content based object retrieval. This problem is often cast in terms of the shapes intrinsic geometry due to its invariance to a wide range of non-rigid deformations. In this paper, we devise a novel generative model for shape retrieval based on the spectral representation of the ...
Andrea Gasparetto +2 more
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Random effects models with non‐parametric priors
Statistics in Medicine, 1992AbstractWe discuss the performance of non‐parametric maximum likelihood (NPML) estimators for the distribution of a univariate random effect in the analysis of longitudinal data. For continuous data, we analyse generated and real data sets, and compare the NPML method to those that assume a Gaussian random effects distribution and to ordinary least ...
S M, Butler, T A, Louis
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A non-parametric approach to behavioral device modeling
2010 11th International Symposium on Quality Electronic Design (ISQED), 2010This work proposes a non-parametric methodology for quick and effective behavioral macromodeling of complex digital and analog devices. Gaussian Process Regression (GPR) learning algorithms are used to generate simple, robust, and widely applicable time-domain models without specifying device equations or parameters.
Dragoljub Gagi Drmanac +2 more
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Non-parametric habitat models with automatic interactions
Journal of Vegetation Science, 2006AbstractQuestions:Can a statistical model be designed to represent more directly the nature of organismal response to multiple interacting factors? Can multiplicative kernel smoothers be used for this purpose? What advantages does this approach have over more traditional habitat modelling methods?Methods:Non‐parametric multiplicative regression (NPMR ...
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A non-parametric Bayesian model for bounded data
Pattern Recognition, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thanh Minh Nguyen 0001 +1 more
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Non-parametric models for spatial efficiency
Journal of Productivity Analysis, 1995This research develops a nonconvex model for measuring the spatial efficiency of siting decisions and demonstrates the virtues of such measurements in comparison to those of convex approaches. Working with a case study from the public sector, we develop relative spatial efficiency (RSE) models which access the sufficiency of a location decision in ...
Antreas D. Athanassopoulos +1 more
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Non Parametric Models for Covergence Analysis in Europe
2004[RELAZIONE]
PACINI, BARBARA, PELLEGRINI G.
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A non-parametric model for Ballistocardiography
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014Yu Yao +3 more
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Local Likelihood for Non-Parametric Arch(1) Models
Journal of Time Series Analysis, 2003We propose a local likelihood estimation for the log-transformed ARCH(1) model in the financial field. Our nonparametric estimator is constructed within the likelihood framework for non-Gaussian observations: It is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed.
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