Perturbed Datasets Methods for Hypothesis Testing and Structure of Corresponding Confidence Sets

Hypothesis testing methods that do not rely on exact distribution assumptions have been emerging lately. The method of sign-perturbed sums (SPS) is capable of characterizing confidence regions with exact confidence levels for linear regression and linear dynamical systems parameter estimation problems if the noise distribution is symmetric. This paper describes a general family of hypothesis testing methods that have an exact user chosen confidence level based on finite sample count and without relying on an assumed noise distribution. It is shown that the SPS method belongs to this family and we provide another hypothesis test for the case where the symmetry assumption is replaced with exchangeability. In the case of linear regression problems it is shown that the confidence regions are connected, bounded and possibly non-convex sets in both cases. To highlight the importance of understanding the structure of confidence regions corresponding to such hypothesis tests it is shown that confidence sets for linear dynamical systems parameter estimates generated using the SPS method can have non-connected parts, which have far reaching consequences

An identification approach to dynamic errors-in-variables systems with a preliminary clustering of observations

Errors-in-variables models are statistical models in which not only dependent but also independent variables are observed with error, i.e. they exhibit a symmetrical model structure in terms of noise. The application field for these models is diverse including computer vision, image reconstruction, speech and audio processing, signal processing, modal and spectral analysis, system identification, econometrics and time series analysis. This paper explores applying the errors-in-variables approach to parameter estimation of discrete-time dynamic linear systems. In particular, a framework is introduced in which a preliminary separation step is applied to group observations prior to parameter estimation. As a result, instead of one, two sets of estimates are derived simultaneously, comparing which can yield estimates for noise parameters. The proposed approach is compared to other schemes with simulation examples

Improved topic identification for similar document search on mobile devices

This paper presents a novel, two level classifier ensemble designed to support document topic identification in mobile device environments. The proposed system aims at supporting mobile device users who search for documents located in other mobile devices which have similar topic to the documents on the users own device. Conforming to the environment of mobile devices, the algorithms are designed for slower processor, smaller memory capacity and they maintain small data traffic between the devices in order to keep low the cost of communication. We propose a keyword list based topic comparison, enhanced with a two level classifier ensemble to accelerate the topic identification process. The new technique enables document topic comparison using few communication traffic and it requires few calculations

Topic comparison of remote documents using small communication traffic

This paper presents a new method for semantic search solutions designed for mobile device environments. The proposed system aims at helping users by searching for documents which have similar topics to the ones stored on the users own device. The search is performed in background continuously and the user is notified if documents worth for downloading were found. The methods proposed in this paper aim at solving this task while maintaining low communication traffic to make them applicable in the mobile device environment

Application Layer Anycast

In this paper, we present a new approach to application layer anycasting. The key to anycast is making it possible for clients to efficiently find the `best´ server for a given application in an unknown group of servers. The anycast service makes a wide range of new multimedia applications possible, and will be part of future integrated services networks. We designed a selective anycast protocol, which is aimed at picking the right server based on application specific metrics, such as network delay and server load. This paper considers server-choosing metrics and efficient mechanisms to compute these metrics. We also present simulation results, which show our approach´s merit, and proves that anycast can significantly improve the performance as compared to the traditional methods