Results 261 to 270 of about 21,802,969 (303)

Optimal solution of the two-stage Kalman estimator

Proceedings of 1995 34th IEEE Conference on Decision and Control, 1999
The two-stage Kalman estimator was originally proposed to reduce the computational complexity of the augmented state Kalman filter. It was also applied to the tracking of maneuvering targets by treating the target acceleration as a bias term. Except in certain restrictive conditions, the conventional two-stage estimators are suboptimal in the sense ...
Chien-Shu Hsieh, Fu-Chuang Chen
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On Two–stage Estimate Based on Independent Estimate of Covariance Matrix

Acta Mathematica Sinica, English Series, 2005
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yin, Suju, Wang, Songgui
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A two-stage estimation method for depth estimation of facial landmarks

IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015), 2015
To address the problem of 3D face modeling based on a set of landmarks on images, the traditional feature-based morphable model, using face class-specific information, makes direct use of these 2D points to infer a dense 3D face surface. However, the unknown depth of landmarks degrades accuracy considerably. A promising solution is to predict the depth
Xun Gong 0002, Zehua Fu, Xinxin Li, Lin Feng   +3 more
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Robust Fault Diagnosis with a Two-stage Kalman Estimator

European Journal of Control, 1997
In the first part of this paper, we present a two-stage Kalman filter for state and bias filtering in dynamic stochastic systems affected by unknown inputs and constant biases. It is shown that the state estimate can be expressed as x k / k = x k / k + β k / k b k / k , where the biasfree estimate x k
L. Summerer, Jean-Yves Keller, Mohamed Darouach   +2 more
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TWO‐STAGE SUPPORT ESTIMATION

Australian & New Zealand Journal of Statistics, 2005
SummaryThis paper presents a two‐stage procedure for estimating the conditional support curve of a random variable X, given the information of a random vector X. Quantile estimation is followed by an extremal analysis on the residuals for problems which can be written as regression models.
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Estimating the Mean by Two-Stage Sampling with Replacement

Calcutta Statistical Association Bulletin, 1963
Summary In two-stage sampling when sampling is done with replacement at each stage, a better estimate of the population mean can be obtained by considering the distinct units only at each stage. The mean of the distinct uniss is chosen as the desired estimate and an exact expression for the variance is obtained. A sufficient condition
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Two Stage Estimation in Data Processing

2008 International Symposium on Intelligent Information Technology Application Workshops, 2008
The new method presented in this paper shows an effective way of solving an estimation problem, the estimated values are nearer to their theoretical ones than in an adjustment with the method of least squares. Considering the semi parametric adjustment models, firstly, the estimators of the parameters and the nonparametric are derived by using a kernel
Pan Xiong, Chen Gang, Chen Yu
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The Nonlinear Two-Stage Least-Squares Estimator

Journal of Econometrics, 1974
In this paper we consider estimation of the parameters of a single equation of a simultaneous equations model which is nonlinear both in variables and paarmeters. Such a model has never been analyzed in the literature to the best of our knowledge. Models in which the nonlinearity appears only in variables or only in parameters have been previously ...
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Two-stage estimation after parameter selection

2016 IEEE Statistical Signal Processing Workshop (SSP), 2016
In many practical multiparameter estimation problems, no a-priori information exists regarding which parameters are more relevant within a group of candidate unknown parameters. This paper considers the estimation of a selected “parameter of interest”, where the selection is conducted according to a data-based selection rule, Ψ.
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Two Stages Liu Regression Estimator

Communications in Statistics - Simulation and Computation, 2015
This paper introduces a new estimator for multicollinearity and autocorrelated errors. We propose the Two Stages Liu estimator (TL) for the multiple linear regression model which suffers from autocorrelation AR(1) and multicollinearity problems. We use a mixed method to apply the two stages least squares procedure (TS) for deriving the TL estimator. We
Issam Dawoud, Selahattin Kaçiranlar
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