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 30 for “"Maximum Likelihood Estimation (MLE)"”.
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Confidence Intervals for the Difference and Ratio of Two Means from Independent Beta Distribution
… from two independent Beta distributions using Maximum Likelihood Estimation (MLE) and the Wald method. It addresses the limitations of traditional methods due to the unique properties of Beta distributions. Extensive simulation studies assess interval performance based on coverage probability …
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High-fidelity simulation of load balancing methods in Tor
… across the Tor network. The second is using the maximum likelihood estimation (MLE) scheme to accurately estimate the true capacity of relay capacities with minimal prior knowledge. For both algorithms, we presented the technical implementation details and the experiment setup. In Tightrope's …
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Population size estimation from mark-resighting surveys
… above mentioned sampling scheme we present the maximum likelihood estimation (MLE) and Bayesian estimation of N by using different methods and construct their confidence intervals. We also use real data sets and simulated data sets to test these statistical indexes, and we find that it is …
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A Bayesian approach to crossed-random-effects mediation analysis for zero-inflated mediators and binary outcomes
… mediator, and a binary outcome. With maximum likelihood estimation (MLE), the mediation model could not converge, which was consistent with the previous finding on analyzing CREM with MLE (Huang & Anderson, 2020). Therefore, the current study investigated whether Bayesian estimation …
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Inverse uncertainty quantification of trace physical model parameters using Bayesian analysis
… input model parameter uncertainties based on Maximum Likelihood Estimation (MLE), Bayesian Maximum A Priori (MAP), and Markov Chain Monte Carlo (MCMC) algorithm for physical models using relevant experimental data. The objective of the present work is to perform the sensitivity analysis of the …
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A Bayesian solution to non-convergence of crossed random effects models
… effects of the subjects and stimuli; however, maximum likelihood estimation (MLE) and restricted maximum likelihood (REML) estimation often encounter convergence problems, which in turn lead to researchers fitting simpler models (e.g., only random intercepts). If the random effect structure is …
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Determinants of non-farm self-employment in rural Virginia
… of labor force participation, and then the likelihood of being self-employed. The probit equations are estimated by the maximum likelihood estimation (MLE) procedure, using LIMDEP, an econometrics program. The Statistical Analysis Systems (SAS) is used for descriptive analysis and …
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Impacts of great western development on agricultural production in the west of China
… approach with several economic theories such as maximum likelihood estimation (MLE), measurement of technical inefficiency and estimation of technical change in the production function. An important contribution of this thesis is empirical estimation of stochastic frontier production function for …
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Properties of Weighted Generalized Beta Distribution of the Second Kind
… generalized beta-F family of distributions, and maximum likelihood estimation (MLE) is used to obtain the parameter estimates. WGB2 is applied as descriptive models for the size distribution of income, and fitted to U.S. family income (2001- 2009) data with different values of parameters. The …
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Single View Coplanar Photogrammetry and Uncertainty Analysis for Traffic Accident Reconstruction
… and exterior orientation of the camera. Process maximum likelihood estimation (MLE) of the interior parameters of the camera or camera calibration and methods to provide uncertainty for those parameters are discussed. To find the maximum likelihood estimation of the exterior orientation of the …
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Loglinear Models as Item Response Models
… research on the development of an accompanying estimation method. Historically, a significant barrier to the application of log-linear models in analyzing item responses has been the high computational cost of maximum likelihood estimation (MLE), due to the fact that the number of response …
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Generating regular expressions from natural language specifications: A semantics-based approach and an empirical study
… learning model using a syntax-based objective: maximum likelihood estimation (MLE). Such syntax-based approaches do not effectively address the goal of generating semantically correct programs, because these approaches fail to handle Program Aliasing, i.e., semantically equivalent programs may …
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Maximum likelihood parameter estimation in time series models using sequential Monte Carlo
… best, a procedure generally called parameter estimation. This thesis comprises novel contributions to the methodology on parameter estimation in time series models. Our primary interest is online estimation, although batch estimation is also considered. The developed methods are based on batch …
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Offline Reward Learning from Human Demonstrations and Feedback: A Linear Programming Approach
… work in reward learning has employed the maximum likelihood estimation (MLE) approach, relying on prior knowledge or assumptions about decision or preference models. However, such dependencies can lead to robustness issues, particularly when there is a mismatch between the presupposed …
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Analysis of equity and interest rate returns in South Africa under the context of jump diffusion processes
… data only. The methods used are the standard Maximum Likelihood Estimation (MLE) approach, the likelihood profiling method of Honore (1998), the Method of Moments Estimation (MME) technique and the Expectation Maximisation (EM) algorithm. The calibration methods are applied to both simulated …
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Fiducial Inference for Mixed-Effects Models: A Frequentist Advancement for Small-Sample Problems
… settings. Traditional methods such as maximum likelihood estimation (MLE), Wald-type intervals, and bootstrap techniques often fail to provide accurate interval estimates when sample sizes are limited—a common scenario in epidemiologic studies, rare disease trials, and environmental …
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Optimal and Suboptimal Signal Detection-On the Relationship Between Estimation and Detection Theory
… the Minimal Invariant Group (MIG) while the maximum information of the observed signal is preserved. We prove that any invariant test with respect to the MIG is CFAR. Then, we introduce the UMP-CFAR test as the optimal CFAR bound among all CFAR tests. In the third part, the asymptotical …
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Essays on the Bayesian inequality restricted estimation
Bayesian estimation has gained ground after Markov Chain Monte Carlo process made it possible to sample from exact posterior distributions. This research aims at contributing to the ongoing debate about the relative virtues of the Frequentist and Bayesian theories by concentrating on the …
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Can oracle-based imitation learning improveneural machine translation with dataaggregation?
… a MTmodel directly. For the training usually the maximum likelihood estimation (MLE) is used,such that the likelihood of each token in the output, given the input sequence, is maximized.This creates a discrepancy between training (MLE) and validation objective (BLEU). Thisthesis tries to overcome …
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Knowledge Discovery from Complex Event Time Data with Covariates
… lifetime data with covariates. Reliability estimation based on complete failure-time data or failure-time data with certain types of censoring has been extensively studied in statistics and engineering. However, the actual failure times of individual components are usually unavailable in …
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