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 9 of 9 for “"Non Gaussian data"”.
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An Ensemble of Novel Techniques for Non-Linear, Non-Gaussian Data Assimilation
Data assimilation (DA) presents a theoretically rigorous framework for combining measurement data from real-world processes with simulations that attempt to mimic the said process. DA is a challenging task due to (i) computationally expensive, but uncertain model simulations, (ii) spatiotemporal …
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Statistical approaches for spatial prediction and anomaly detection
… parameter estimation for irregularly spaced, and non-Gaussian data. It will be shown that by judiciously replacing the likelihood with an empirical likelihood in the Bayesian hierarchical model, approximate posterior distributions for the mean and covariance parameters can be obtained. Due to the …
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Variable selection for generalized linear mixed models and non-Gaussian Genome-wide associated study data
… variable selection methods BG2 and IBG3 for non-Gaussian GWAS data. To solve ultra-high dimension problem and highly correlated SNPs problem, BG2 and IBG3 have two steps: screening step and fine-mapping step. In the screening step, BG2 and IBG3, like SMA method, only have one SNP in one model …
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Markov chain Monte Carlo methodoloy for inference with generalised linear spatial models
… geostatistics where a continuous spatial phenomenon is modelled through an underlying latent Gaussian process. If the observed data are also Gaussian then inference for the underlying process and the model parameters is relatively straightforward. In many applications though the assumption of …
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Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling
… complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes. To address these challenges, …
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Hypothesis Testing Using Spatially Dependent Heavy-Tailed Multisensor Data
… variety of natural and man-made signals, sensor data from many different applications, in fact, are characterized by non-Gaussian distributions. A common characteristic observed in non-Gaussian data is the presence of heavy-tails or fat tails. For such data, the probability density function …
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Statistical approaches to leak detection for geological sequestration
… to detect shifts in the mean of atmospheric CO₂ data. Because the data are uncertain, statistical approaches are necessary. The traditional way to detect a shift would be to apply a hypothesis test, such as Z- or t-tests, directly to the data. These methods implicitly assume the data are Gaussian …
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Prediction and Anomaly Detection Techniques for Spatial Data
… on environmental issues, huge amounts of spatial data have been collected from location based social network applications to scientific data. This has encouraged formation of large spatial data set and generated considerable interests for identifying novel and meaningful patterns. Allowing …
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Advanced Sampling Methods for Solving Large-Scale Inverse Problems
… as the two main approaches for solving data assimilation and inverse problems. The majority of the methods in these two approaches are derived (at least implicitly) under the assumption that the underlying probability distributions are Gaussian. It is well accepted, however, that the …