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- All Subjects: Bayesian statistical decision theory
- Creators: Ahn, Seung
Description
This dissertation applies the Bayesian approach as a method to improve the estimation efficiency of existing econometric tools. The first chapter suggests the Continuous Choice Bayesian (CCB) estimator which combines the Bayesian approach with the Continuous Choice (CC) estimator suggested by Imai and Keane (2004). Using simulation study, I provide two important findings. First, the CC estimator clearly has better finite sample properties compared to a frequently used Discrete Choice (DC) estimator. Second, the CCB estimator has better estimation efficiency when data size is relatively small and it still retains the advantage of the CC estimator over the DC estimator. The second chapter estimates baseball's managerial efficiency using a stochastic frontier function with the Bayesian approach. When I apply a stochastic frontier model to baseball panel data, the difficult part is that dataset often has a small number of periods, which result in large estimation variance. To overcome this problem, I apply the Bayesian approach to a stochastic frontier analysis. I compare the confidence interval of efficiencies from the Bayesian estimator with the classical frequentist confidence interval. Simulation results show that when I use the Bayesian approach, I achieve smaller estimation variance while I do not lose any reliability in a point estimation. Then, I apply the Bayesian stochastic frontier analysis to answer some interesting questions in baseball.
ContributorsChoi, Kwang-shin (Author) / Ahn, Seung (Thesis advisor) / Mehra, Rajnish (Committee member) / Park, Sungho (Committee member) / Arizona State University (Publisher)
Created2014
Description
Pipeline infrastructure forms a vital aspect of the United States economy and standard of living. A majority of the current pipeline systems were installed in the early 1900’s and often lack a reliable database reporting the mechanical properties, and information about manufacturing and installation, thereby raising a concern for their safety and integrity. Testing for the aging pipe strength and toughness estimation without interrupting the transmission and operations thus becomes important. The state-of-the-art techniques tend to focus on the single modality deterministic estimation of pipe strength and do not account for inhomogeneity and uncertainties, many others appear to rely on destructive means. These gaps provide an impetus for novel methods to better characterize the pipe material properties. The focus of this study is the design of a Bayesian Network information fusion model for the prediction of accurate probabilistic pipe strength and consequently the maximum allowable operating pressure. A multimodal diagnosis is performed by assessing the mechanical property variation within the pipe in terms of material property measurements, such as microstructure, composition, hardness and other mechanical properties through experimental analysis, which are then integrated with the Bayesian network model that uses a Markov chain Monte Carlo (MCMC) algorithm. Prototype testing is carried out for model verification, validation and demonstration and data training of the model is employed to obtain a more accurate measure of the probabilistic pipe strength. With a view of providing a holistic measure of material performance in service, the fatigue properties of the pipe steel are investigated. The variation in the fatigue crack growth rate (da/dN) along the direction of the pipe wall thickness is studied in relation to the microstructure and the material constants for the crack growth have been reported. A combination of imaging and composition analysis is incorporated to study the fracture surface of the fatigue specimen. Finally, some well-known statistical inference models are employed for prediction of manufacturing process parameters for steel pipelines. The adaptability of the small datasets for the accuracy of the prediction outcomes is discussed and the models are compared for their performance.
ContributorsDahire, Sonam (Author) / Liu, Yongming (Thesis advisor) / Jiao, Yang (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2018