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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Assessing the effects of coronary artery bypass grafting versus complex cardiac surgery: Comparison of methods of adjusting for channeling bias

Liu, Yanyun January 2010 (has links)
<p>Coronary artely bypass grafting (CABG) is the most commnonly performed "open heart" operation in North America. Complex cardiac surgeries served for a large amount of the cardiac surgery population, but outcomes after these surgeries have been limited by lack of appropriate interpretation. Given the observed trend toward an increasing proportion of complex cardiac surgeries, there is a great need to understand the outcomes and patterns of resource utilization for the population who have had complex cardiac surgery.</p> <p>The clinical objectives of this thesis are to compare clinical outcomes and resource usage between isolated coronary bypass grafting and complex cardiac surgery and detelmine the difference of outcomes for complex cardiac surgeries among cardiac surgical sites across Canada.</p> <p>The statistical objective of this thesis is to compare Bayesian and classical methods of analyzing two surgeries difference in outcomes. The classical methods are multivariable logistic regression, matched propensity score method, propensity score weighted regression and stratified propensity score method. The Bayesian method is Bayesian matched propensity score.</p> <p>For the primary outcome mortality, the odds ratio and 95% confidence interval for the treatment effect is 4.49 (1.92, 10.56) for propensity score matching method, 4.97 (3.62, 6.11) for propensity score weight method, 3.49 (1.91, 6.40) for propensity score strata method, 3.71 (2.10, 6.56) for multivariab1e regression method, and 3.82 (1.23, 13.07) for Bayesian propensity score matching method. Different methods obtained different treatment effect estimates.</p> <p>We concluded that patients who are undergoing complex cardiac surgery have a greater risk for adverse postoperative events and longer ICU length of stay compared to patients who are undergoing isolated CABG. We also found that there is variability in<br />outcomes and resource usage among Canadian cardiac centers.</p> / Master of Science (MS)
2

Likelihood-based inference for Tweedie generalized linear models

Dunn, P. Unknown Date (has links)
No description available.
3

A New Approximation Scheme for Monte Carlo Applications

Jones, Bo 01 January 2017 (has links)
Approximation algorithms employing Monte Carlo methods, across application domains, often require as a subroutine the estimation of the mean of a random variable with support on [0,1]. One wishes to estimate this mean to within a user-specified error, using as few samples from the simulated distribution as possible. In the case that the mean being estimated is small, one is then interested in controlling the relative error of the estimate. We introduce a new (epsilon, delta) relative error approximation scheme for [0,1] random variables and provide a comparison of this algorithm's performance to that of an existing approximation scheme, both establishing theoretical bounds on the expected number of samples required by the two algorithms and empirically comparing the samples used when the algorithms are employed for a particular application.
4

Statistical Theory for Adversarial Robustness in Machine Learning

Yue Xing (14142297) 21 November 2022 (has links)
<p>Deep learning plays an important role in various disciplines, such as auto-driving, information technology, manufacturing, medical studies, and financial studies. In the past decade, there have been fruitful studies on deep learning in which training and testing data are assumed to follow the same distribution to humans. Recent studies reveal that these dedicated models are vulnerable to adversarial attack, i.e., the predicting label may be changed even if the testing input has an unaware perturbation. However, most existing studies aim to develop computationally efficient adversarial learning algorithms without a thorough understanding of the statistical properties of these algorithms. This dissertation aims to provide theoretical understandings of adversarial training to figure out potential improvements in this area of research. </p> <p><br></p> <p>The first part of this dissertation focuses on the algorithmic stability of adversarial training. We reveal that the algorithmic stability of the vanilla adversarial training method is sub-optimal, and we study the effectiveness of a simple noise injection method. While noise injection improves stability, it also does not deteriorate the consistency of adversarial training.</p> <p><br></p> <p>The second part of this dissertation reveals a phase transition phenomenon in adversarial training. When the attack strength increases, the training trajectory of adversarial training will deviate from its natural counterpart. Consequently, various properties of adversarial training are different from clean training. It is essential to have adaptations in the training configuration and the neural network structure to improve adversarial training.</p> <p><br></p> <p>The last part of this dissertation focuses on how artificially generated data improves adversarial training. It is observed that utilizing synthetic data improves adversarial robustness, even if the data are generated using the original training data, i.e., no extra information is introduced. We use a theory to explain the reason behind this observation and propose further adaptations to utilize the generated data better.</p>
5

A New Right Tailed Test of the Ratio of Variances

Lesser, Elizabeth Rochelle 01 January 2016 (has links)
It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.
6

Investigating quantitative genetic issues for a pedigree plant breeding program using computer simulation

Jensen, N. M. Unknown Date (has links)
No description available.
7

Programa de estadísticas agrícolas en el Valle del Cauca

Palacios M., Graciela. Roa M., Carlos, January 1963 (has links)
"Tesis de grado que presentan como requisito parcial para optar al título de economistas." / Bibliography: leaves 90-91.
8

On Some Test Statistics for Testing the Population Skewness and Kurtosis: An Empirical Study

Guo, Yawen 26 August 2016 (has links)
The purpose of this thesis is to propose some test statistics for testing the skewness and kurtosis parameters of a distribution, not limited to a normal distribution. Since a theoretical comparison is not possible, a simulation study has been conducted to compare the performance of the test statistics. We have compared both parametric methods (classical method with normality assumption) and non-parametric methods (bootstrap in Bias Corrected Standard Method, Efron’s Percentile Method, Hall’s Percentile Method and Bias Corrected Percentile Method). Our simulation results for testing the skewness parameter indicate that the power of the tests differs significantly across sample sizes, the choice of alternative hypotheses and methods we chose. For testing the kurtosis parameter, the simulation results suggested that the classical method performs well when the data are from both normal and beta distributions and bootstrap methods are useful for uniform distribution especially when the sample size is large.
9

Policy Evaluation in Statistical Reinforcement Learning

Pratik Ramprasad (14222525) 07 December 2022 (has links)
<p>While Reinforcement Learning (RL) has achieved phenomenal success in diverse fields in recent years, the statistical properties of the underlying algorithms are still not fully understood. One key aspect in this regard is the evaluation of the value associated with the RL agent. In this dissertation, we propose two statistically sound methods for policy evaluation and inference, and study their theoretical properties within the RL setting. </p> <p><br></p> <p>In the first work, we propose an online bootstrap method for statistical inference in policy evaluation. The bootstrap is a flexible and efficient approach for inference in online learning, but its efficacy in the RL setting has yet to be explored. Existing methods for online inference are restricted to settings involving independently sampled observations. In contrast, our method is shown to be distributionally consistent for statistical inference in policy evaluation under Markovian noise, which is a standard assumption in the RL setting. To demonstrate the effectiveness of our method in practical applications, we include several numerical simulations involving the temporal difference (TD) learning and Gradient TD (GTD) learning algorithms across a range of real RL environments. </p> <p><br></p> <p>In the second work, we propose a tensor Markov Decision Process framework for modeling the evolution of a sequential decision-making process when the state-action features are tensors. Under this framework, we develop a low-rank tensor estimation method for off-policy evaluation in batch RL. The proposed estimator approximates the Q-function using a tensor parameter embedded with low-rank structure. To overcome the challenge of nonconvexity, we introduce an efficient block coordinate descent approach with closed-form solutions to the alternating updates. Under standard assumptions from the tensor and RL literature, we establish an upper bound on the statistical error which guarantees a sub-linear rate of computational error. We provide numerical simulations to demonstrate that our method significantly outperforms standard batch off-policy evaluation algorithms when the true parameter has a low-rank tensor structure.</p>
10

Length scales in granular matter

Zhao, Song-Chuan 28 February 2013 (has links)
No description available.

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