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This thesis involves three essays that explore the theory and application of econometric analysis to labour market interventions. One essay is methodological, and two essays are applications. The first essay contributes to the literature on inference with data sets containing within-cluster correlation. The essay highlights a problem with current practices when the number of clusters is 11 or fewer. Current practices can result in p-values that are not point identified but are instead p-value intervals. The chapter provides Monte Carlo evidence to support a proposed solution to this problem. The second essay analyzes a labour market intervention within Canada--the Youth Hires program--which aimed to reduce youth unemployment. We find evidence that the program was able to increase employment among the targeted group. However, the impacts are only present for males, and we find evidence of displacement effects amongst the non-targeted group. The third essay examines a set of Graduate Retention Programs that several Canadian provinces offer. These programs are aimed at mitigating future skill shortages. Once the solution proposed in the first essay is applied, I find little evidence of the effectiveness of these programs in attracting or retaining recent graduates. / Thesis (Ph.D, Economics) -- Queen's University, 2013-07-05 15:56:33.805

We explore both robust biologically guided intensity-modulated radiation therapy (BG-IMRT) and pattern recognition to identify responders to cancer treatment for lung cancer. Heterogeneous dose prescriptions that are derived from biological images are subject to uncertainty, due to potential noise in the image. We develop a robust optimization model to design BG-IMRT plans that are de-sensitized to uncertainty. Computational results show improvements in tumor control probability and deviation from prescription dose compared to a non-robust model, while maintaining tissue dose below toxicity levels. We applied machine learning algorithms to 4D gated positron emission tomography/computed tomography (PET/CT) scans. We identified classifiers which could outperform a naive classifier. Our work shows the potential of using machine learning algorithms to predict patient response. This could hopefully lead to more adaptive treatment plans, where the clinician would adapt the treatment based on the prediction provided at certain time intervals in the treatment.

We explore both robust biologically guided intensity-modulated radiation therapy (BG-IMRT) and pattern recognition to identify responders to cancer treatment for lung cancer. Heterogeneous dose prescriptions that are derived from biological images are subject to uncertainty, due to potential noise in the image. We develop a robust optimization model to design BG-IMRT plans that are de-sensitized to uncertainty. Computational results show improvements in tumor control probability and deviation from prescription dose compared to a non-robust model, while maintaining tissue dose below toxicity levels. We applied machine learning algorithms to 4D gated positron emission tomography/computed tomography (PET/CT) scans. We identified classifiers which could outperform a naive classifier. Our work shows the potential of using machine learning algorithms to predict patient response. This could hopefully lead to more adaptive treatment plans, where the clinician would adapt the treatment based on the prediction provided at certain time intervals in the treatment.

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This thesis clarifies, develops, and applies Brennan and Buchanan's concept of robust political economy.

Robust statistics is an extension of classical parametric statistics, which provides a safeguard against gross errors in experiments. Effectively, robustness properties of Uhlig's Q-estimators are examined and compared with that. of Rocke's Ai-estimators. In particular, the finite-sample implosion and explosion breakdown points are inves-tigated and introduced into constructing robust designs for the one-way random effects model. Optimal robust designs based on Uhlig's Q-estimation are similar to the ones based on Rocke's M-estimation. Ultimately. robust estimation procedures would provide steady and reliable estimates of model parameters in case of the occurrence of outliers.

In this thesis we introduce and study an A-optimal minimax design criterion for two-level fractional factorial designs, which can be used to estimate a linear model with main effects and some interactions. The resulting designs are called A-optimal minimax designs, and they are robust against the misspecification of the terms in the linear model. They are also efficient, and often they are the same as A-optimal and D-optimal designs. Various theoretical results about A-optimal minimax designs are derived. A couple of search algorithms including a simulated annealing algorithm are discussed to search for optimal designs, and many interesting examples are presented in the thesis. / Graduate / 0463 / yinyue@uvic.ca

This dissertation proposes a power management and control strategy for islanded microgrids, which consist of multiple electronically-interfaced distributed energy resource (DER) units, to achieve a prescribed load sharing scheme. This strategy provides i) a power management system to specify voltage set points based on a classical power flow analysis; 2) DER local controllers, designed based on a robust, decentralized, servomechanism approach, to track the set points; and 3) a frequency control and synchronization scheme. This strategy is then generalized to incorporate both power-controlled and voltage-controlled DER units. Since the voltage-controlled DER units do not use inner current control loops, they are vulnerable to overcurrent/overload transients subsequent to system severe disturbances, e.g., faults and overloading conditions. To prevent DER unit trip-out or damage under these conditions, an overcurrent/overload protection scheme is proposed that detects microgrid abnormal conditions, modifies the terminal voltage of the corresponding VSC to limit DER unit output current/power within the permissible range, and restores voltage controllers subsequently. Under certain circumstances, e.g., microgrid islanding and communication failure, there is a need to switch from an active to a latent microgrid controller. To minimize the resultant transients, control transition should be performed smoothly. For the aforementioned two circumstances, two smooth control transition techniques, based on 1) an observer and 2) an auxiliary tracking controller, are proposed to achieve a smooth control transition. A typical microgrid system that adopts the proposed strategy is investigated. The microgrid dynamics are investigated based on eigenvalue sensitivity and robust analysis studies to evaluate the performance of the closed-loop linearized microgrid. Extensive case studies, based on time-domain simulations in the PSCAD/EMTDC platform, are performed to evaluate performance of the proposed controllers when the microgrid is subject to various disturbances, e.g., load change, DER abrupt outage, configuration change, faults, and overloading conditions. Real-time hardware-in-the-loop case studies, using an RTDS system and NI-cRIO industrial controllers, are also conducted to demonstrate ease of hardware implementation, validate controller performance, and demonstrate its insensitivity to hardware implementation issues, e.g., noise, PWM nonidealities, A/D and D/A conversion errors and delays.