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 7 of 7 for “"Parametric inference"”.
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Advanced Bayesian methodologies for parametric and non-parametric inference of spatiotemporal phenomena
DSpace SAF Submission Ingestion Package generated from Vireo submission #21468 on 2025-03-28 at 14:55:33
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Density Estimation for Robust Financial Econometrics
Chapter 3 introduces an efficient and robust parametric inference which minimizes the Hellinger distance between two nonparametrically smoothed density estimates: the simulated model density and corresponding observed density. This approach generalizes work of Beran (1977) and Basu and Lindsay …
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Nonparametric Bayesian Modelling in Machine Learning
Nonparametric Bayesian inference has widespread applications in statistics and machine learning. In this thesis, we examine the most popular priors used in Bayesian non-parametric inference. The Dirichlet process and its extensions are priors on an infinite-dimensional space. Originally introduced …
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The Genetics of General Cognitive Ability
… their sample-size-corrected AIC, and based our parametric inference on model-averaged point estimates and standard errors. Taken as a whole, these three studies demonstrate that GCA is substantially heritable and massively polygenic, but it is also influenced by environmental factors, and its …
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Genomic applications of statistical signal processing
… theoretic methods are applied for non-parametric inference. Bayesian methods are adopted to incorporate several sources with prior knowledge. This work aims to construct an inference system which takes into account different sources of information such that the absence of some …
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Experimental evaluation of the efficiencies of certain non- parametric statistics
… The use of the 10% level of significance in non-parametric tests does seem unrealistic, because, in general, non-parametric statistics tend to be more conservative than parametric statistics. In case non-parametric methods are applied to samples from a population which is normally distributed, …
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Fiducial Inference for Mixed-Effects Models: A Frequentist Advancement for Small-Sample Problems
… dissertation develops a comprehensive fiducial inference framework for mixed-effects models, offering a robust frequentist alternative for statistical inference in small-sample settings. Traditional methods such as maximum likelihood estimation (MLE), Wald-type intervals, and bootstrap …