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 6 of 6 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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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 …