Adaptive Estimation of a Conditional Density

2015 ◽  
Vol 84 (2) ◽  
pp. 291-316 ◽  
Author(s):  
Ann-Kathrin Bott ◽  
Michael Kohler
Keyword(s):  
Adaptive Estimation ◽  
Conditional Density

scholarly journals Adaptive Estimation in ARCH Models

1993 ◽  
Vol 9 (4) ◽  
pp. 539-569 ◽  
Author(s):  
Oliver Linton
Keyword(s):  
Regression Model ◽  
Functional Form ◽  
Adaptive Estimation ◽  
Conditional Density ◽  
Arch Models ◽  
Error Density ◽  
The Mean

We construct efficient estimators of the identifiable parameters in a regression model when the errors follow a stationary parametric ARCH(P) process. We do not assume a functional form for the conditional density of the errors, but do require that it be symmetric about zero. The estimators of the mean parameters are adaptive in the sense of Bickel [2]. The ARCH parameters are not jointly identifiable with the error density. We consider a reparameterization of the variance process and show that the identifiable parameters of this process are adaptively estimable.

scholarly journals A Note on the Adaptive Estimation of a Conditional Continuous-Discrete Multivariate Density by Wavelet Methods

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Christophe Chesneau ◽  
Hassan Doosti
Keyword(s):  
Simulation Study ◽  
Wide Class ◽  
Adaptive Estimation ◽  
Rates Of Convergence ◽  
Conditional Density ◽  
Adaptive Estimator ◽  
Practical Performance ◽  
Multivariate Density ◽  
Wavelet Methods ◽  
Theoretical Performance

We investigate the estimation of a multivariate continuous-discrete conditional density. We develop an adaptive estimator based on wavelet methods. We prove its good theoretical performance by determining sharp rates of convergence under the Lp risk with p≥1 for a wide class of unknown conditional densities. A simulation study illustrates the good practical performance of our estimator.

A reliable adaptive estimation method for the installation error of optical angular encoder in outfield tests

Measurement ◽  
2021 ◽  
Vol 174 ◽  
pp. 109035
Author(s):  
Xuxing Zhao ◽  
Renjian Feng ◽  
Yinfeng Wu ◽  
Ning Yu ◽  
Xiaofeng Meng ◽  
...  
Keyword(s):  
Adaptive Estimation ◽  
Estimation Method

scholarly journals Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
Sergey Sokolov ◽  
Arthur Novikov ◽  
Marianna Polyakova
Keyword(s):  
Covariance Matrix ◽  
Adaptive Estimation ◽  
Observation System ◽  
Mobile Object ◽  
New Approach ◽  
Measurement Systems ◽  
System Parameters ◽  
Traditional Scheme ◽  
Adaptive Filtration ◽  
Measurement Noises

In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman’s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer’s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.

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