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Showing 1 to 8 of 8 for “"Functional time series"”.
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Changepoint detection and estimation for spatially indexed functional time series
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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Test of change point versus long-range dependence in functional time series
In scalar time series analysis, a long-range dependent (LRD) series cannot be easily distinguished from certain non-stationary models, such as the change in mean model with short-range dependent (SRD) errors. To be specific, realizations of LRD series usually have a characteristic of changing local …
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Likelihood Ratio Combination of Multiple Biomarkers and Change Point Detection in Functional Time Series
… in medical research. Change point detection for functional time series has attracted considerable attention from researchers. Existing methods either rely on FPCA, which may perform poorly with complex data, or use bootstrap approaches in forms that fall short in effectively detecting diverse …
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Bayesian population inference for effective connectivity
… (MAR) process is proposed to jointly model functional neuroimaging time series collected from multiple subjects, and to characterize the distribution of MAR coefficients across the population from which those subjects were drawn. Thus, model-based inference about the interaction between …
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Shape based classification and functional forecast of traffic flow profiles
… accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors.</p><p>To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using …
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Statistical inference for dependent data
Functional data Analysis has emerged as an important area of statistics which provides convenient and informative tool for the analysis of data objects of high dimension/high resolution. In the literature, it seems that the emphasis has been placed on independent functional data or models where the …
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Bayesian analysis of historical functional linear models with application to air pollution forecasting
Historical functional linear models are used to analyse the relationship between a functional response and a functional predictor whereby only the past of the predictor process can affect the current outcome. In this work, we develop a Bayesian framework for the analysis of the historical …
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STATISTICAL MODELING FOR COMPLEX FUNCTIONAL AND NETWORK TIME SERIES DATA
Functional and network time series data have been available, which provide richer information that brings opportunities and potentials to solve scientific problems. On the other hand, the complex structure presents challenges to conventional statistical analytical tools in terms of estimation and …