Bias and variance for small-sample error estimation are typically posed in terms of statistics for the distributions of the true and estimated errors. On the other hand, a salient practical issue asks, given an error estimate, what can be said about the true error? This question relates to the joint distribution of the true and estimated errors, specifically, the conditional expectation of the ...

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. Th...

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters x observed through a known linear transformation H and corrupted by additive noise. The CSLS estimator is a biased estimator directed at improving the performance of the traditional least-squares (LS) estimator by choosing the e...

Multihypothesis motion-compensating predictors combine several motioncompensated signals to predict the current frame of a video signal. This paper applies the wide-sense stationary theory of multihypothesis motion compensation for hybrid video codecs to multihypothesis motion estimation. This allows us to study the influence of the displacement error correlation on the efficiency of multihypot...

Patient motion compensation is challenging for dynamic 2D/3-D overlay in interventional procedures. A first motion compensation approach based on depth-layers has been recently proposed, where 3-D motion can be estimated by tracking feature points on 2-D X-ray images. However, the sparse depth estimation introduces a systematic error. In this paper, we present a theoretical analysis on the syst...

The mean-variance model for portfolio selection requires estimates of many parameters. This paper investigates the effect of errors in parameter estimates on the results of mean-variance analysis. Using a small amount of historical data to estimate parameters exposes the model to estimation errors. However, using a long time horizon to estimate parameters increases the possibility of nonstation...

Abstract: This paper deals with the state estimation of linear time-invariant discrete systems with unknown inputs. The forward sequences of the output are treated as additional outputs. In this case, the rank condition for designing the unknown input estimator is relaxed. The gain for minimal estimation error variance is presented, and a numerical example is given to verify the proposed unknow...

This paper introduces a method that computes an estimation of the bit error rate (BER) based on the RAKE receiver soft output only. For this method no knowledge is needed about the channel characteristics nor the precise external conditions. Simulations show that the mean error of the estimation is below 2%, with only a small variance. Also an estimation of the BER for a different spreading fac...

In most previous research work on data detection in mobile radio systems the noise is assumed to be Gaussian distributed with known noise variance, and a separate variance estimation is required in realistic scenarios. In order to avoid the additional variance estimator and to obtain better system performance, a novel optimum minimum mean square error (MMSE) detector exploiting noise statistics...