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Distance Estimation (distance + estimation)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Perceptual 3D pose distance estimation by boosting relational geometric features

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 2-3 2009
Cheng Chen
Abstract Traditional pose similarity functions based on joint coordinates or rotations often do not conform to human perception. We propose a new perceptual pose distance: Relational Geometric Distance that accumulates the differences over a set of features that reflects the geometric relations between different body parts. An extensive relational geometric feature pool that contains a large number of potential features is defined, and the features effective for pose similarity estimation are selected using a set of labeled data by Adaboost. The extensive feature pool guarantees that a wide diversity of features is considered, and the boosting ensures that the selected features are optimized when used jointly. Finally, the selected features form a pose distance function that can be used for novel poses. Experiments show that our method outperforms others in emulating human perception in pose similarity. Our method can also adapt to specific motion types and capture the features that are important for pose similarity of a certain motion type. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Minimum , -divergence estimation for arch models

JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2006
S. Ajay Chandra
Abstract., This paper considers a minimum , -divergence estimation for a class of ARCH(p) models. For these models with unknown volatility parameters, the exact form of the innovation density is supposed to be unknown in detail but is thought to be close to members of some parametric family. To approximate such a density, we first construct an estimator for the unknown volatility parameters using the conditional least squares estimator given by Tjøstheim [Stochastic processes and their applications (1986) Vol. 21, pp. 251,273]. Then, a nonparametric kernel density estimator is constructed for the innovation density based on the estimated residuals. Using techniques of the minimum Hellinger distance estimation for stochastic models and residual empirical process from an ARCH(p) model given by Beran [Annals of Statistics (1977) Vol. 5, pp. 445,463] and Lee and Taniguchi [Statistica Sinica (2005) Vol. 15, pp. 215,234] respectively, it is shown that the proposed estimator is consistent and asymptotically normal. Moreover, a robustness measure for the score of the estimator is introduced. The asymptotic efficiency and robustness of the estimator are illustrated by simulations. The proposed estimator is also applied to daily stock returns of Dell Corporation. [source]

A note on penalized minimum distance estimation in nonparametric regression

THE CANADIAN JOURNAL OF STATISTICS, Issue 3 2003
Florentina Bunea
Abstract The authors introduce a penalized minimum distance regression estimator. They show the estimator to balance, among a sequence of nested models of increasing complexity, the L1 -approximation error of each model class and a penalty term which reflects the richness of each model and serves as a upper bound for the estimation error. Les auteurs présentent un nouvel estimateur de régression obtenu par minimisation d'une distance pénalisée. Ils montrent que pour une suite de modèles embo,tés à complexité croissante, cet estimateur offre un bon compromis entre l'erreur d'approximation L1 de chaque classe de modèles et un terme de pénalisation permettant à la fois de refléter la richesse de chaque modèle et de majorer l'erreur d'estimation. [source]

Frequency-detected acoustic ranging solutions in wireless sensor networks: an experimental study

ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2008
Jiming Chen
Abstract Ranging is a basic distance estimation for many range-based localization approaches, which are important to wireless sensor networks (WSNs) applications at many levels. In this paper, we propose a simple frequency-detected based time difference of arrival (FD-TDoA), which can be implemented by detecting acoustic frequency to compute the time of flight in air. Furthermore, we put forward a new acoustic ranging solution named time of arrival (TOA)2, which can be applied to WSNs with asynchronous nodes. Unlike other published works, the design of TOA2 uses a bidirectional acoustic signal exchange between a pair of communication nodes. This technique is significantly simple and effective. The latency between the time at which the Mica2 is commanded to emit an acoustic pulse and the earliest possible time that can be detected anywhere, is considered in our solutions. The error of these ranging solutions, the correction expressions by fitted lines and the sensitivity on hardware are analyzed by a large number of experiments based on a resource-constrained Mica2 hardware platform. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd. [source]