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 159 for “"Resampling"”.
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Importance Resampling for Global Illumination
… Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration for common rendering problems. We show how to select the importance resampling parameters for near …
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Regular and irregular signal resampling
In this thesis, we consider three main resampling problems. The first is the sampling rate conversion problem in which the input and output grids are both regularly spaced. It is known that the output signal is obtained by applying a time-varying filter to the input signal. The existing methods for …
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Application of bootstrap resampling in fMRI
This thesis demonstrates the use of the bootstrap resampling technique considering temporal dependency in the fMRI data to determine the reliability and confidence interval of fMRI parameters. Traditionally, the test-retest method has been used to reliably detect active voxels in the fMRI image of …
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Optimization, random resampling, and modeling in bioinformatics
… a challenging problem. In this work, I present a resampling-based approach to obtain biological functional categories from gene set and apply the approach to analyze lithium-sensitivity of neurological diseases and cancer. Then, the non-parametrical permutation-based approach is applied to …
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Parametric Resampling Methods for Retrospective Changepoint Analysis
Changepoint analysis is a useful tool in environmental statistics in that it provides a methodology for threshold detection and modeling processes subject to periodic changes in the underlying model due to anthropogenic effects or natural phenomena. Several applications of changepoint analysis are …
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Novel Hybrid Resampling Algorithms for Parallel/Distributed Particle Filters
… multiple processing units (PUs). The classical resampling algorithm used in parallel/distributed particle filters is a centralized scheme, called centralized resampling, which needs a central unit (CU) to serve as a hub for data transfers. As a result, the centralized resampling procedures …
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An information-theoretic analysis of resampling in Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods form a popular class of Bayesian inference algorithms. While originally applied primarily to state-space models, SMC is increasingly being used as a general-purpose Bayesian inference tool. Traditional analyses of SMC algorithms focus on their usage for …
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Software implementation of a video resampling algorithm on the TMS320C80
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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Properties of a Resampling Validation Technique for Empirically Scoring Psychological Assessments
… to examine the psychometric properties of a new, resampling-based validity estimator which does not require a crossvalidation sample in order to produce unbiased estimates of validity. The first and second experiments were Monte Carlo simulations while the third and fourth applied the method to …
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Analysis of signal resampling effects on attention-driven SEI for IoT systems
The rapid growth of the Internet of Things (IoT) has connected billions of devices, many with minimal security, making them vulnerable to attacks. Specific Emitter Identification (SEI) offers a passive and reliable security solution by identifying devices through their unique hardware features, …
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Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.
… have several advantages over other common resampling methods. First, the undersampling amount is independent of class imbalance and proportional to the degree of overlap. This could effectively address the problem of class overlap while reducing the effect of class imbalance. Second, …
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Eliciting Bootstrapping: The Development of Introductory Statistics Students’ Informal Inferential Reasoning via Resampling
… and developed reasoning of sampling, the resampling process of bootstrapping, and inference. The focus of my analysis was on the model development of four focus groups of students. Groups of students constructed models of sampling and inference that they used to collect samples, aggregate …
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Lung carcinogenesis modeling: Resampling and simulation approach to model fitting, validation, and prediction
Because of serious health implications, lung cancer is the leading cancer killer for both men and women. It is well known that smoking is the major risk factor for lung cancer. I propose to use a two-stage clonal expansion (TSCE) model to evaluate the effects of smoking on initiation and promotion …
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Evaluation and assignment of significance levels to peptide identifications from the database search programs using resampling approach
A novel application of Monte Carlo permutation testing that improves the calculation of the peptide match significance levels and detection rate in database search programs is demonstrated. Novel k-permuted decoy databases (where k denotes the type and number of permutations) were evaluated for …
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Functional Anova Models With Application to Corporate Bonds
… we proposed four different approaches for resampling data under the null hypothesis of no effect of the factors. They include bootstrapping method with two separate resampling approaches: resampling residuals at each time point; and resampling residuals curves. The other approaches were …
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Statistical analysis of networks with community structure and bootstrap methods for big data
… studies. Bootstrap methods for big data: Resampling methods provide a powerful method of evaluating the precision of a wide variety of statistical inference methods. The complexity and massive size of big data makes it infeasible to apply traditional resampling methods for big data. In the …
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Accelerated coplanar facet radio synthesis imaging
… normally computed by performing a convolutional resampling step and applying the Inverse Fast Fourier Transform, because this leads to computational savings. Unfortunately, the resulting planar approximation of the sky is only valid over small regions. When imaging over wider fields of view, and …
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Aplicação de técnicas estatísticas de reamostragem em hidrologia
… Both Bootstrap and Jackknife are called Resampling Methods. The Bootstrap is applied to decide the amount of information needed for modeling the one hundred year flood. It is shown that the resampling techniques are reasonably accurate methods and can be applied to define confidence …
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Towards smooth particle filters for likelihood estimation with multivariate latent variables
… used for each filter. This is because the same resampling step which drastically reduces the variance of the estimates also introduces discontinuities in the particles that are selected across filters when the parameters change. When the state variables are univariate, a method exists that gives …
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