Results 221 to 230 of about 316,835 (263)

Averaging sampling: Models and properties

2008 American Control Conference, 2008
In the paper we study the properties of a sampling method for stochastic signals corrupted by a wide-band stochastic noise where samples are taken as average values of the signal over the sampling interval to diminish the influence of noise. We also study possible improvement attained by discrete-time Kalman filtering applied to the sampled signal.
Marian J. Blachuta, Rafal T. Grygiel
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Averaging in the Autoresonance Model

Mathematical Notes, 2003
The author investigates perturbed Hamiltonian systems for \(u(t, \varepsilon)\), \(v(t,\varepsilon)\) of the form \[ du/dt-H_v(u,v)=\varepsilon f(\tau)\cos\varphi,\qquad dv/dt+H_u(u,v)=\varepsilon g(\tau)\cos\varphi, \] with initial conditions \(u(0,\varepsilon)=v(0,\varepsilon)=0\), where \(\varepsilon\) is a small parameter, \(\tau=\varepsilon t\) is
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Bayesian Model Selection and Model Averaging

Journal of Mathematical Psychology, 2000
This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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Averaged modeling of the cardiovascular system

52nd IEEE Conference on Decision and Control, 2013
In approximating nonlinear systems, averaging theory provides very useful tools, especially for periodic/quasi-periodic systems. In the case of complex dynamical systems (e.g. biological systems), such an approach leads to consistent simplifications of the encountered mathematical models.
Alexandru Codrean, Toma-Leonida Dragomir
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Model Averaging for Regression Kink Models

Statistical Analysis and Data Mining: An ASA Data Science Journal
ABSTRACT This article investigates optimal model averaging for regression kink models with heteroscedasticity. When all candidate models are misspecified, we establish the asymptotic optimality of the corresponding model averaging estimator in the sense of minimizing the squared prediction loss.
Li Xi, Xiyuan Zhang, Yuting Wei, Zhanshou Chen   +3 more
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Model averaging with privacy-preserving

Communications in Statistics - Simulation and Computation, 2019
Model averaging approach is an effective technique in alleviating the risk of misspecification and improving prediction accuracy for datasets with high dimension.
Baihua He, Fangli Dong
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On threshold moving‐average models [PDF]

Journal of Time Series Analysis, 1998
In this paper the class of discrete self‐exciting threshold moving‐average (SETMA) models is studied in some detail. In particular, we consider various problems associated with the identification, estimation and testing of these models. A simple method for distinguishing between low order moving average (MA) and low order SETMA models is presented ...
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An Averaging Model for Self-Esteem

Psychological Reports, 1991
Weighted averaging is proposed as a speculative model for self-esteem. Each aspect of personality is weighted by its subjective importance. High self-esteem is held to obtain when high weights are attached to traits with high scale values. Therapy designed to increase self-esteem does so by bringing the patients' weights and scale values into alignment.
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Ensemble averaging in turbulence modelling

Physics Letters A, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Grmela, Miroslav, Jou, David, Casas-Vazquez, Jose, Bousmina, Mosto, Lebon, Georgy   +4 more
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Frequentist Model Averaging in Structural Equation Modelling

Psychometrika, 2019
Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference.
Jin, Shaobo, Ankargren, Sebastian
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