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» Model selection by sequentially normalized least squares
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FS
2010
140views more  FS 2010»
13 years 6 months ago
Nonparametric estimation for a stochastic volatility model
Abstract Consider discrete time observations (X δ)1≤ ≤n+1 of the process X satisfying dXt = √ VtdBt, with Vt a one-dimensional positive diffusion process independent of the...
F. Comte, V. Genon-Catalot, Yves Rozenholc
ICASSP
2011
IEEE
12 years 11 months ago
Maximum a posteriori based regularization parameter selection
The 1 norm regularized least square technique has been proposed as an efficient method to calculate sparse solutions. However, the choice of the regularization parameter is still...
Ashkan Panahi, Mats Viberg
CSDA
2010
157views more  CSDA 2010»
13 years 7 months ago
Robust estimation of constrained covariance matrices for confirmatory factor analysis
Confirmatory factor analysis (CFA) is a data anylsis procedure that is widely used in social and behavioral sciences in general and other applied sciences that deal with large qua...
E. Dupuis Lozeron, M. P. Victoria-Feser
WSC
2008
13 years 10 months ago
The knowledge-gradient stopping rule for ranking and selection
We consider the ranking and selection of normal means in a fully sequential Bayesian context. By considering the sampling and stopping problems jointly rather than separately, we ...
Peter Frazier, Warren B. Powell
BC
2004
91views more  BC 2004»
13 years 7 months ago
Simulation and parameter estimation of dynamics of synaptic depression
Abstract. Synaptic release was simulated using a Simulink sequential storage model with three vesicular pools. Modeling was modular and easily extendable to the systems with greate...
F. Aristizabal, M. I. Glavinovic