Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 35 for “"Change point detection"”.
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High-dimensional change point detection for mean and location parameters
Change point inference refers to detection of structural breaks of a sequence observation, which may have one or more distributional shifts subject to models such as mean or covariance changes. In this dissertation, we consider the offline multiple change point problem that the sample size is fixed …
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Gaussian processes for state space models and change point detection
… combine Gaussian processes and Bayesian online change point detection (BOCPD) to increase the generality of the Gaussian process time series methods. These methodologies are evaluated on predictive performance on six real world data sets, which include three environmental data sets, one …
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Change Point Detection and Estimation in Sequences of Dependent Random Variables
<p>Two change point detection and estimation procedures for sequences of dependent binary random variables are proposed and their asymptotic properties are explored. The two procedures are a dependent cumulative sum statistic (DCUSUM) and a dependent likelihood ratio test (LRT) statistic, which are …
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Change Point Detection in Time Series via Multivariate Singular Spectrum Analysis
The objective of change-point detection (CPD) is to estimate the time of significant and abrupt changes in the dynamics of a system through multivariate time series observations. The setup of CPD covers a wide range of real-world problems such as quality control, medical diagnosis, speech …
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A Non-Parametric Approach to Change-Point Detection in Cross-Asset Correlations
… this thesis we explore the problem of detecting change-points in cross-asset correlations using a non-parametric approach. We began by comparing and contrasting several common methods for change-point detection as well as methods for measuring correlation. We finally settle on a statistic …
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Change point detection for high dimensional data and valid inference for Bayesian linear models
… statistical methodologies for high dimensional change point detection and inference for Bayesian linear models. In the first project, we propose a change point detection method testing mean shift for high dimensional observations with unknown heteroscedasticity. The proposed tests target a dense …
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Likelihood Ratio Combination of Multiple Biomarkers and Change Point Detection in Functional Time Series
… for future applications 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 …
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Variance Change Point Detection under A Smoothly-changing Mean Trend with Application to Liver Procurement
Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario that the variance of data may make a significant jump while the mean of data changes in a smooth fashion. It is …
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Statistical inference in complex networks: community detection, change-point detection, link prediction, and two-sample testing
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01
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Advancements in test security: preventive test assembly methods and change-point detection of compromised items in adaptive testing
… proposes several augmented methods for the detection of compromised items, all involving simultaneous monitoring of changes in both the proportion correct and average response time for each operational item in the pool. Simulation results indicate that the consideration of response times can …
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Time series analysis for event detection in text data
… of different text mining problems. Event detection is the general task of finding any emerging events, such as significant changes in stock price, anomalies in climate data, and outbreaks of a certain disease, depending on the data we are interested in. While in text mining, event …
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Assessing annual urban change and its impacts on evapotranspiration
Land Use Land Cover Change (LULCC) is a major component of global environmental change, which could result in huge impacts on biodiversity, water yield and quality, climate, soil condition, food security and human welfare. Of all the LULCC types, urbanization is considered to be the most impactful …
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Adaptive Estimation and Detection Techniques with Applications
… been successfully applied to the fields of fault detection and identification as well as target tracking in this thesis. In particular, an integrated framework to detect, identify and estimate failures is developed based on the VSMM. It can handle sequential failures and multiple failures by …
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Towards robust inference for Bayesian filtering of linear Gaussian dynamical systems subject to additive change
… we propose a formalisation in the frame work of change point detection and join ideas from the popular CUSUM algo- rithm with ideas from Bayesian online change point detection to combine frequent reliability constraints and online inference resulting in a Gaussian mixture model variant of …
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Simulating Dynamical Systems from Data
… include time series imputation and forecasting, change point detection, reinforcement learning, and trace-driven simulation in networked systems. Exploiting the principled factorization structure has paved the way for the contributions we make in different tasks. First, we propose and analyze …
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Monitoring Parameter Change in Autocorrelated Logistic Regression
Monitoring changes in health care performances, financial markets, and industrial processes has recently gained momentum due to increased capability of computers and the availability of real-time data collection and storage software. As a consequence, there has been a growing demand in developing …
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Application Of Statistical Methods In Risk And Reliability
… function F(z) and its inverse at some fixed points z and u on the basis of an i.i.d. sample where the sample size is relatively small. The sample is modeled as having the flexible Generalized Gamma distribution with all three parameters being unknown. This approach can be viewed as an …
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On the Analysis of DNA Methylation
… novel approach to DNA methylation analysis using change point detection instead of estimating methylation level directly. However, we show that current change-point detection methods are not robust to methylation signal, we therefore explore how to extend current non-parametric methods to …
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Advanced computational techniques for pipe burst detection and localisation in water distribution networks
… deep learning (FL-DL) architectures for accurate detection and developing real-time localisation methods using Change point detection (CPD) algorithms. The research is structured in two main phases to tackle these challenges. The first phase conducts a comparative analysis of hyperparameter …
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Statistical learning methods for mining marketing and biological data
… model real world data with temporal or spatial changes. First, a collaborated online change-point detection method is proposed to identify the change-points in sparse time series. It leverages the signals from the auxiliary time series such as engagement metrics to compensate the sparse revenue …
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