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 16 of 16 for “"Robust Inference"”.
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Robust Inference in Multiple Nonstationary Time Series
In addition, a multivariate generalization of the unit root tests proposed in Zivot and Andrews (1992) is investigated. The null hypothesis entails the existence of a number of cointegrating vectors band no breaks in the constant term of the vector autoregression. Under the alternative, there are …
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Robust Inference via Optimal Transport Ambiguity Sets
… used statistical algorithms with distributional robustness. The Kalman filter enables accurate, real-time tracking of latent states by assimilating noisy, indirect measurements over time. Its performance relies on precise state-space models for both the evolution dynamics and the observation …
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MITO: ROBUST INFERENCE OF MITOCHONDRIAL PHYLOGENIES AND CLONES
… for flexible preprocessing of scLT data, lineage inference, and interactive exploration of MT-SNV-derived phylogenies and clonal structures. MiTo integrates seamlessly with popular single-cell analysis libraries, filling a significant gap in the scLT community. To benchmark MiTo, we generated a …
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Towards robust inference for Bayesian filtering of linear Gaussian dynamical systems subject to additive change
… frequent reliability constraints and online inference resulting in a Gaussian mixture model variant of multiple Kalman filters. We hereby exploit open-end sequential probability ratio tests on the evidence of Kalman filters on observation sub-sequences for aggregated inference under notions …
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Leveraging biological pathways and gene networks to understand the genetic architecture of diseases and complex traits
… accounting for known annotations is critical to robust inference of biological mechanisms.
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Certifying robustness in inference and learning problems
There is a rich literature of algorithms for inference, prediction, and decision-making problems when the underlying distributions governing the data are known and well-modeled. The research from the past few decades has provided powerful learning algorithms when such distributions cannot be easily …
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Modeling and inference of the dynamics of spatiotemporally evolving systems using evolving gaussian processes
… Gaussian Processes (E-GP), for modeling and inference of spatiotemporally evolving dynamical systems. We show that an E-GP model can be used to estimate the latent state of large-scale physical systems of this type, and furthermore that a single E-GP model can generalize over multiple …
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Monte Carlo Methods for Motion Planning and Goal Inference
… of the observed agent, and utilize Bayesian inference to reason about the underlying end goals of their movement. We propose a sequential Monte Carlo method that adapts the MCMC trajectory sampling to construct plausible hypotheses about the agent’s motion plan and then updates these …
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Statistical inference for dependent data
… and autocorrelation consistent based robust inference, we propose a class of estimators for estimating the asymptotic covariance matrix of the generalized method of moments estimator in the stationary time series models in Chapter 3. Under mild conditions, we establish the first order …
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On the evaluation and application of neural language models for grammatical error detection
… use of linguistic signals that would facilitate robust and generalisable GED performance. First, I investigate whether information from different types of neural language model can be transferred to models for GED. I evaluate five models against three publicly available ESL benchmarks, and report …
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Prediction and Anomaly Detection Techniques for Spatial Data
… tasks, including spatial outlier detection, robust inference for spatial dataset, and spatial prediction for large multivariate non-Gaussian data. spatial outlier analysis, which aims at detecting abnormal objects in spatial contexts, can help extract important knowledge in many applications. …
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Essays on misspecified models
… when moments are misspecified and provide robust estimators and test statistics when moment conditions are misspecified. Chapter 1, ”Robust Inference for Instrumental Variable Models with Locally Non-exogenous Instruments”, highlights that conventional tests often fail to give accurate …
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The Universe through a magnifying glass: Precision cosmology with CMB lensing
… measuring this lensing signal will enable robust tests of the standard cosmological model via the comparison of high-precision measurements of structure growth at late times with predictions. This thesis explores several uses and applications of CMB lensing. On the theory side, we construct …
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Modularized Bayesian Inference: Methodology, Algorithm, Theory And Application.
Bayesian inference has shown powerful impacts on understanding and explaining data and their generating mechanisms, but misspecification of the model is a major threat to the validity of the inference. Although methods that deal with misspecification have been developed and their properties have …
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Data Summarizations for Scalable, Robust and Privacy-Aware Learning in High Dimensions
… and can be used for general purpose Bayesian inference under real-world constraints, including privacy preservation and robustness to outliers, encompassing diverse uncertainty-aware data analysis tasks, such as density estimation, classification and regression. We motivate the necessity for …
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Certifiably correct SLAM
… presents a serious barrier to the development of robust autonomous systems generally. To address this problem, in this thesis we develop a suite of algorithms for SLAM that preserve the computational efficiency of existing state-of-the-art methods while additionally providing explicit performance …