scholarly journals Term Structure Estimation in Low-Frequency Transaction Markets: A Kalman Filter Approach with Incomplete Panel-Data

2004 ◽  
Author(s):  
Gonzalo Cortazar ◽  
Eduardo S. Schwartz ◽  
Lorenzo Naranjo
Keyword(s):  
Kalman Filter ◽  
Panel Data ◽  
Term Structure ◽  
Low Frequency ◽  
Structure Estimation ◽  
Filter Approach

Wavelet Filter Approach for GPS Attitude Tracking System on Ship

2010 ◽  
Vol 450 ◽  
pp. 552-555
Author(s):  
Ping Wang ◽  
Kai Xue ◽  
Qiu Hong Li
Keyword(s):  
High Frequency ◽  
Tracking System ◽  
Tracking Error ◽  
Low Frequency ◽  
Attitude Measurement ◽  
Visual Effects ◽  
Error Sources ◽  
Attitude Tracking ◽  
Servo Mechanism ◽  
Filter Approach

GPS attitude tracking system on the ship is a servo mechanism which could be used for counteracting the effects of the ship’s pitch and roll. But the attitude measurement precision of ship is more important to the tracking precision of the servo mechanism. As one of the major error sources, the noises of GPS attitude measurement bring out the steady tracking error of the tracking servo mechanism. To reduce the steady error due to the noise, the threshold noise removing method of wavelet is used to eliminate the noise. And the better result with the meaning of standard deviation and the better visual effects could be gotten by using the method. The signals of the processed high frequency and the retained low frequency could be reconstructed with the original signals. Therefore, the signals after noise removing could be obtained. The threshold noise removing method of wavelet used to remove the noise of GPS attitude information in the paper is of great value in practice.

Explaining the term structure of interest rates: A panel data approach

1996 ◽  
Vol 48 (1) ◽  
pp. 11-21 ◽  
Author(s):  
E.Scott Mayfield ◽  
Robert G. Murphy
Keyword(s):  
Panel Data ◽  
Interest Rates ◽  
Term Structure

scholarly journals Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model

2012 ◽  
Vol 140 (8) ◽  
pp. 2628-2646 ◽  
Author(s):  
Shu-Chih Yang ◽  
Eugenia Kalnay ◽  
Brian Hunt
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Linear Models ◽  
Low Frequency ◽  
Lorenz Model ◽  
Outer Loop ◽  
Nonlinear Growth ◽  
Ensemble Mean ◽  
Ensemble Transform Kalman Filter ◽  
The One

Abstract An ensemble Kalman filter (EnKF) is optimal only for linear models because it assumes Gaussian distributions. A new type of outer loop, different from the one used in 3D and 4D variational data assimilation (Var), is proposed for EnKF to improve its ability to handle nonlinear dynamics, especially for long assimilation windows. The idea of the “running in place” (RIP) algorithm is to increase the observation influence by reusing observations when there is strong nonlinear error growth, and thus improve the ensemble mean and perturbations within the local ensemble transform Kalman filter (LETKF) framework. The “quasi-outer-loop” (QOL) algorithm, proposed here as a simplified version of RIP, aims to improve the ensemble mean so that ensemble perturbations are centered at a more accurate state. The performances of LETKF–RIP and LETKF–QOL in the presence of nonlinearities are tested with the three-variable Lorenz model. Results show that RIP and QOL allow LETKF to use longer assimilation windows with significant improvement of the analysis accuracy during periods of high nonlinear growth. For low-frequency observations (every 25 time steps, leading to long assimilation windows), and using the optimal inflation, the standard LETKF RMS error is 0.68, whereas for QOL and RIP the RMS errors are 0.47 and 0.35, respectively. This can be compared to the best 4D-Var analysis error of 0.53, obtained by using both the optimal long assimilation windows (75 time steps) and quasi-static variational analysis.

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