Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.798357
Title: Adaptive multiscale approaches to regression and trend segmentation
Author: Maeng, Hyeyoung
ISNI:       0000 0004 8507 2598
Awarding Body: London School of Economics and Political Science (LSE)
Current Institution: London School of Economics and Political Science (University of London)
Date of Award: 2019
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Abstract:
Data-adaptive modelling has enjoyed increasing popularity across a wide range of statistical problems. This thesis studies three adaptive multiscale approaches, one in regression and two in trend segmentation. We first introduce a way of modelling temporal dependence in random functions, assuming that those random curves are discretised on an equispaced grid. Considering a common dependence structure across the discretised curves, we predict the most recent point from the past observations in the framework of linear regression. Our model partitions the regression parameters into a smooth and a rough regime where rough regression parameters are used for observations located close to the response variable while the set of regression coefficients for the predictors positioned far from the response variable are assumed to be sampled from a smooth function. The smoothness change-point and the regression parameters are jointly estimated, and the asymptotic behaviour of the estimated change-point is presented. The performance of our new model is illustrated through simulations and four real data examples including country fertility data, pollution data, stock volatility series and sunspot number data. Secondly, we study the detection of multiple change-points corresponding to linear trend changes or point anomalies in one-dimensional data. We propose a data-adaptive multiscale decomposition of the data through an unbalanced wavelet transform, hoping that the sparse representation of the data is achieved through this decomposition. The entire procedure consists of four steps and we provide a precise recipe of each.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.798357  DOI: Not available
Keywords: QA Mathematics
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