Adaptive Estimation of a Conditional Density

2015 ◽  
Vol 84 (2) ◽  
pp. 291-316 ◽  
Author(s):  
Ann-Kathrin Bott ◽  
Michael Kohler
1993 ◽  
Vol 9 (4) ◽  
pp. 539-569 ◽  
Author(s):  
Oliver Linton

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.


2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Christophe Chesneau ◽  
Hassan Doosti

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.


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

Inventions ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 10
Author(s):  
Sergey Sokolov ◽  
Arthur Novikov ◽  
Marianna Polyakova

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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