Results 271 to 280 of about 16,220,352 (310)

Non-parametric Model for Background Subtraction

2000
Background subtraction is a method typically used to segment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can handle situations where the background of the scene is cluttered
Ahmed M. Elgammal, David Harwood, Larry S. Davis   +2 more
openaire   +1 more source

A Bayesian non-parametric stochastic frontier model

Annals of Tourism Research, 2021
In this paper, we introduce a new Bayesian non-parametric stochastic frontier (SF) model that addresses the endogeneity problem and relaxes problematic assumptions regarding functional form, and distributional properties. The model can be seen as a competitor to DEA.
Assaf, A. George, Tsionas, Mike, Kock, Florian, Josiassen, Alexander   +3 more
openaire   +3 more sources

Simulated Non-Parametric Estimation of Dynamic Models

Review of Economic Studies, 2009
Summary: This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the ...
Filippo Altissimo, Antonio Mele
openaire   +3 more sources

Non-parametric models

2001
This chapter presents a range of statistical techniques that are available for the analysis of word frequency distributions. Section 2.1 introduces some basic probabilistic concepts. Section 2.2 discusses the urn model, according to which word use is viewed as random selection from a population with fixed probabilities for words to occur.
openaire   +1 more source

Modeling by Non-Parametric Regression

1997
In the previous chapter on the adaptive modeling of natural laws it was stated that tasks associated with such modeling included the estimation and storage of the probability distribution, as well as the development of a method for its effective application.
Igor Grabec, Wolfgang Sachse
openaire   +1 more source

Non-parametric identification of generalized Hammerstein models

International Journal of Systems Science, 1998
The 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, Kamal A. F. Moustafa   +1 more
openaire   +2 more sources

Non-parametric Spectral Model for Shape Retrieval

2015 International Conference on 3D Vision, 2015
Non-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, Giorgia Minello, Andrea Torsello   +2 more
openaire   +1 more source

Random effects models with non‐parametric priors

Statistics in Medicine, 1992
AbstractWe 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
openaire   +2 more sources

Non-parametric habitat models with automatic interactions

Journal of Vegetation Science, 2006
AbstractQuestions: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 ...
openaire   +1 more source

A non-parametric approach to behavioral device modeling

2010 11th International Symposium on Quality Electronic Design (ISQED), 2010
This 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, Brendon Bolin, Li-C. Wang   +2 more
openaire   +2 more sources