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 65 for “"Non-Stationarity"”.
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Quickest change detection under post-change non-stationarity and uncertainty
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms
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Quantile Inference and Change Point Test under Time Series Non-stationarity
Recently, the non-stationary time series data attracts increased attention from researchers. The main goal of the thesis is to develop the methodologies for quantile inference and change point test under time series non-stationarity. The first part of the thesis considers the simultaneous or …
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Essays on Testing Hypotheses When Non-stationarity Exists in Panel Data Models
… on testing hypotheses in panel data models when non-stationarity exists in the model. This is done under the high-dimensional framework where both n (cross-section dimension) and T (time series dimension) are large. In the first essay, I discuss the limiting distribution of the t-statistic; using …
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AI/ML tools for early decision making in water system operations: managing non-stationarity water quality
… the 'normal state' of the water system. However, non-stationarity events such as wildfires, droughts, and floods shift water systems to new 'states' that negatively impact water quality and complicate water treatment decision-making and performance. Many water system managers do not account for …
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Non-Stationarity, Forecast Performance and Fluctuations in Macroeconomic Series: Experience With United States Seasonal Data and Simulations
… and to the outcomes of the unit root tests. If non-stationarity is present in the series then this framework also enables us to assess the value of the test in the conduct of a forecasting exercise. It is generally agreed that most economic time series contains substantial MA component in the …
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Non-stationarity, forecast performance and fluctuations in macroeconomic series: Experience with United States seasonal data and simulations
… series is generated by a stationary or a non-stationary process. Recent research has shown that there is a seasonal cycle in the US economy that closely mirrors business cycles (Barsky and Miron, 1989). It is thus important to apply the seasonal unit root tests to investigate the question …
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Testing for Structural Change: Evaluation of the Current Methodologies, a Misspecification Testing Perspective and Applications
… modeling has created substantial interest in non- stationarity and its implications for empirical modeling. Beyond the original interest in trend vs. di¤erence non-stationarity, there has been renewed interest in testing and modeling structural breaks. The focus of my dissertation is on …
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Effective Learning in Non-Stationary Multiagent Environments
… simultaneously learn in MARL, leading to natural non-stationarity in the experiences encountered and thus requiring each agent to its behavior with respect to potentially large changes in other agents' policies. This thesis aims to address the non-stationarity challenge in multiagent learning from …
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Using Geovisual Analytics to investigate the performance of Geographically Weighted Discriminant Analysis
… vary spatially. This is also referred to spatial non-stationarity. If spatial non-stationarity exists, GWDA should model the relationship between the categories and predictor variables more accurately, thus resulting in a lower classification uncertainty and ultimately a higher classification …
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Reinforcement Learning in Time-Varying Systems: an Empirical Study
… environment changes over time, i.e. it exhibits non-stationarity. In this work, we characterize the challenges introduced by non-stationarity and develop a framework for addressing them to train RL agents in live systems. Such agents must explore and learn new environments, without hurting the …
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The Influence of Sea-Level Rise on Salinity in the Lower St. Johns River and the Associated Physics
… to adjust, and therefore there is a quantifiable non-stationarity of salinity in the lower St Johns River (shifts in the probability distribution of salinity, as representative of salinity increase) due to sea-level rise. The numerical modeling is validated against data, then the model is applied …
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Modifying a local measure of spatial association to account for non-stationary spatial processes.
… area data sets, many study areas exhibit spatial non-stationarity or spatial variation in mean and variance of observed phenomena. This poses issues for a number of spatial analysis methods which assume data are stationary. The Getis and Ord’s Gi* statistic is a popular measure that, like many …
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Spatial dependency between a linear network and a point pattern
… K-function for point-to-point relationships. The non-stationarity of a linear network is of particular interest in how it affects the measurement of this spatial relationship, which has not been explicitly investigated in the literature before. To investigate this we consider the Poisson line …
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Estimating elasticities of demand and supply for South African manufactured exports using a vector error correction model
… approach in order toaddress simultaneity and non-stationarity issues. Demand is highly price-elastic, ranging from-3 to -6. The price elasticity of supply is 1. Competitors' prices and world income are an important determinant of demand, but domestic capacity utilization is not an important …
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Dynamic model for space-time weather radar observation and nowcasting
… radar measurements over a large area, 2) non-stationarity due to the storm motion, and 3) non-stationarity due to evolution (growth and decay). These difficulties are addressed in this research. To deal with the storm motion, an efficient radar storm tracking algorithm is developed in the …
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State space modelling of extreme values with particle filters
… model that can be used to smoothly capture non-stationarity. Observations are assumed independent given a latent state process so that their distribution can change gradually over time. Sequential Monte Carlo methods known as particle filters provide an approach to inference for such models …
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A generic optimisation framework for reinforcement learning in the foreign exchange market
… such agents is, however, often hindered by the non-stationary nature of financial markets and the inherently opaque decision-making processes of deep reinforcement learning models. An abundance of research has been dedicated to applying deep reinforcement learning in finance, resulting in …
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Understanding Streamflow Change at the Scale of a Major City: Chicago
… development and climate change have made the non-stationarity of stream flow records an emerging phenomenon in hydrology. This thesis studies the systematic shifts in stream flow that have been observed in urban, suburban and agricultural watersheds in and around the Greater Chicago area. A …
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Inference of high-dimensional linear models with time-varying coefficients
… method is based on a novel combination of the nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented …
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