Accurate estimation of the confidence of an indoor localization system is crucial for a number of applications including crowd-sensing applications, map-matching services, and probabilistic location fusion techniques; all of which lead to an enhanced user experience. Current approaches for quantifying the output accuracy of a localization system in real-time either do not provide a distance met...

In this paper, the two-stage procedure is considered for autoregressive parameters estimation in the p-order autoregressive model ( AR(p)). The point estimation and fixed-size confidence ellipsoids construction are investigated which are based on least-squares estimators. Performance criteria are shown including asymptotically risk efficient, asymptotically efficient, and asymptotically consist...

Motivated by the recent work of Herbert, Hayen, Macaskill and Walter [Interval estimation for the difference of two independent variances. Communications in Statistics, Simulation and Computation, 40: 744-758, 2011.], we investigate, in this paper, new confidence intervals for the difference between two normal population variances based on the generalized confidence interval of Weerahandi [Gene...

In this paper we discuss ways to use Bayesian methodology to estimate the matching performance of a biometric identification device when no errors are detected. One of the drawbacks to the classical or frequentist statistical estimation methods is that it is not possible to create a confidence interval for the error rate when no errors are observed. In this paper we begin by discussing the rele...

Inspired by previous work on confidence measures for tempo estimation in loops, we explore ways to add confidence measures to other music labelling tasks. We start by reflecting on the reasons why the work on loops was successful and argue that it is an example of the ideal scenario in which it is possible to define a confidence measure independently of the estimation algorithm. This requires a...