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 17 of 17 for “"Bootstrap resampling"”.
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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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Radio-polarimetric observations of gravitational lenses
… fringe-fit methods in the low SNR regime using bootstrap resampling. Our 43 GHz maps of B0218+357 provide the highest-resolution constraints on the detailed polarization morphology of the background source to date, confirm parity reversal in polarization structure predicted by gravitational …
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Essays on Quantile Regression for Dynamic Panel Data Models
… Schwartz information criterion, and propose a bootstrap resampling procedure for constructing confidence intervals for the parameters of interest. Monte Carlo simulations illustrate the dramatic improvement in the performance of the proposed estimator compared with the fixed effects quantile …
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Modelling breakdown durations in simulation models of engine assembly lines
… the Two-Sample Cram´er-von Mises statistic and bootstrap resampling are used to measure the similarity of two sets of data. We use finite mixture distributions fitted to the breakdown durations data of groups of machines as the input models for the simulation models. We evaluate the complete …
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Weak signal identification and inference in penalized model selection
… proposed method outperforms the perturbation and bootstrap resampling approaches. The method is illustrated for HIV antiretroviral drug susceptibility data to identify genetic mutations associated with HIV drug resistance. We also provide signal's inference method based on the exact distribution …
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Skill or luck: a bootstrapping approach to analysing South African equity unit trust returns
… Fama and French 5-Factor Model. The second is a bootstrap resampling analysis that indicates whether fund performance is better than a randomised (luck-equivalent) distribution of returns, thereby implying manager skill. A regression was run per fund based on the Carhart 4-factor model with the …
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Conditional Quantile Estimation With Ordinal Data
… of the parameter estimates are constructed using bootstrap resampling technique. The Goodman-Kruskal Gamma statistic, a measure of predictive power, is reported for the proposed model and used as a criterion to compare the proposed model with existing approaches. In our simulation study the …
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Ultra-diffuse galaxy candidatesin stripe 82 clusters
… halo mass, N ∝ M1.05±0.45, determined through bootstrap resampling. This shows that the number of UDG candidates increases as the cluster halo mass increases.
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THE INTEGRATED USE OF PHYSICOCHEMICAL AND IN VITRO DATA FOR PREDICTING CHEMICAL TOXICITY
… potential. Finally, novel applications of the bootstrap resampling method were investigated. In particular, algorithms based on this method are shown to provide a means of assessing: a) the minimal variability associated with the Draize rabbit tests for skin and eye irritation; and b) the …
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Mathematical and Deep Learning Methods in High-Energy Physics: From Temporal Variability in M82 ULXs to Spatial Features in Planck Maps
… images—to apply advanced statistical cleaning, bootstrap resampling, and neural-network algorithms that leverage the high information content of modern observations. By combining rigorous time-series analysis with contemporary deep learning tools, this work seeks to improve the reliability of …
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Statistical Methods For Kinetic Modeling Of Fischer Tropsch Synthesis On A Supported Iron Catalyst
… effects was also regressed. Likelihood and bootstrap confidence intervals suggested that the model parameters were precise. Histograms and skewness statistics calculated from Bootstrap resampling show parameter-effect nonlinearities were small.
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A simplified approach to calculating personalized estimates for electric vehicle charging delays
… (NHTS) and performs multiple iterations of bootstrap resampling to create synthetic distributions of long-distance trips within a year. Data related to individual routine vehicle usage and charging delay is unavailable, so we are unable to validate the model’s performance through accuracy …
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Characterizing Student Attention in Technology-Infused Classrooms Using Real-time Active Window Data
… engineering courses with large enrollments. The bootstrap resampling technique is used to estimate mean error rate. Post-tests are used to establish convergent validity by relating learning to active window data. Polytomous logistic regression is used to examine the probability of post-test score …
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Essays on the applications of distributional scaling in finance: Estimation, forecasting and inference
… obtained using an appropriate model-based bootstrap resampling scheme. A series of Monte Carlo exercises demonstrate that the testing procedure possesses good empirical size and power properties in wide range of situations, being robust against various forms of multifractality under the …
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Implementing a cognitive diagnostic assessment in an institutional test: a new networking model in language testing and experiment with a new psychometric model and task type
… so, the study employed a statistical method (resampling technique) as a way to resolve the issue caused by a small size of a test-taker pool at an institution, which is usually smaller than the optimal size for CDA implementation. It also tried to analyze polytomously scored response data. The …
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Analysis and Prediction of the Commuting Mode Choices in England Using Bayesian Networks
… The model’s stability is validated using bootstrap resampling techniques, different data sources, and temporal segmentation of the National Travel Survey dataset, ensuring the reliability of the proposed network structure. This may also shed light on potential improvements in population …
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The Single Imputation Technique in the Gaussian Mixture Model Framework
… In the first imputation procedure, the wild bootstrap is proposed to improve the uncertainty for the residual variance in the regression model. In the second solution, the predictive mean matching (PMM) is enhanced, where the regression model is taking the main role to generate the recipient …