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DOI: 10.5281/zenodo.7788635
title: Stress-induced anisotropic poroelasticity in Westerly granite: Mechanical and Ultrasonic data

description: Data obtained from triaxial loading experiments conducted on thermally cracked Westerly granite which was initially dry and later saturated with water. Text files (.txt) are raw, unprocessed time series of mechanical data including load, shortening and radial and axial strain gauge measurements. Except noted, units are in Volts. Strain gauge voltages can be converted into strain using gauge factor of 2.11 and amplification factor of 40. External shortening can be converted to sample shortening using a machine stiffness of 480 kN/mm. Raw ultrasonic data are combined in folder named BSF*. Data format is binary, and can be read using ASC ltd. software "Insite". Files "sensors.txt" contain ultrasonic sensor positions and time offset corrections. Mechanical and Ultrasonic data are provided for when the sample was dry. Only mechanical data are provided for saturated conditions.
DOI: 10.58119/ulg/uowrsl/oms8h2
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DOI: 10.6084/m9.figshare.16967738
title: Online Estimation for Functional Data

description: Functional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data in a streaming manner. In this article we develop an online nonparametric method to dynamically update the estimates of mean and covariance functions for functional data. The kernel-type estimates can be decomposed into two sufficient statistics depending on the data-driven bandwidths. We propose to approximate the future optimal bandwidths by a sequence of dynamically changing candidates and combine the corresponding statistics across blocks to form the updated estimation. The proposed online method is easy to compute based on the stored sufficient statistics and the current data block. We derive the asymptotic normality and, more importantly, the relative efficiency lower bounds of the online estimates of mean and covariance functions. This provides insight into the relationship between estimation accuracy and computational cost driven by the length of candidate bandwidth sequence. Simulations and real data examples are provided to support such findings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16967738.v2
title: Online Estimation for Functional Data

description: Functional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data in a streaming manner. In this article we develop an online nonparametric method to dynamically update the estimates of mean and covariance functions for functional data. The kernel-type estimates can be decomposed into two sufficient statistics depending on the data-driven bandwidths. We propose to approximate the future optimal bandwidths by a sequence of dynamically changing candidates and combine the corresponding statistics across blocks to form the updated estimation. The proposed online method is easy to compute based on the stored sufficient statistics and the current data block. We derive the asymptotic normality and, more importantly, the relative efficiency lower bounds of the online estimates of mean and covariance functions. This provides insight into the relationship between estimation accuracy and computational cost driven by the length of candidate bandwidth sequence. Simulations and real data examples are provided to support such findings. Supplementary materials for this article are available online.
DOI: 10.58119/ulg/uowrsl/0xlv7o
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DOI: 10.58119/ulg/uowrsl/krllco
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DOI: 10.6084/m9.figshare.16967738.v1
title: Online Estimation for Functional Data

description: Functional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data in a streaming manner. In this article we develop an online nonparametric method to dynamically update the estimates of mean and covariance functions for functional data. The kernel-type estimates can be decomposed into two sufficient statistics depending on the data-driven bandwidths. We propose to approximate the future optimal bandwidths by a sequence of dynamically changing candidates and combine the corresponding statistics across blocks to form the updated estimation. The proposed online method is easy to compute based on the stored sufficient statistics and the current data block. We derive the asymptotic normality and, more importantly, the relative efficiency lower bounds of the online estimates of mean and covariance functions. This provides insight into the relationship between estimation accuracy and computational cost driven by the length of candidate bandwidth sequence. Simulations and real data examples are provided to support such findings.
DOI: 10.58119/ulg/uowrsl/s4bv16
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