Buried pipelines are vulnerable to the threat of corrosion. Hence, they are normally coated with a protective coating to isolate the metal substrate from the surrounding environment with the addition of CP current being applied to the pipeline surface to halt any corrosion activity that might be taking place. With time, this barrier will deteriorate which could potentially lead to corrosion of the pipe. The External Corrosion Direct Assessment (ECDA) methodology was developed with the intention of upholding the structural integrity of pipelines. Above ground indirect inspection techniques such as the DCVG which is an essential part of an ECDA, is commonly used to determine coating defect locations and measure the defect's severity. This is followed by excavation of the identified location for further examination on the extent of pipeline damage. Any coating or corrosion defect found at this stage is repaired and remediated. The location of such excavations is determined by the measurements obtained from the DCVG examination in the form of %IR and subjective inputs from experts which bases their justification on the environment and the physical characteristics of the pipeline. Whilst this seems to be a straight forward process, the factors that comes into play which gave rise to the initial %IR is not fully understood. The lack of understanding with the additional subjective inputs from the assessors has led to unnecessary excavations being conducted which has put tremendous financial strain on pipeline operators. Additionally, the threat of undiscovered defects due to the erroneous nature of the current method has the potential to severely compromise the pipeline's safe continual operation. Accurately predicting the coating defect size (TCDA) and interpretation of the indication signal (%IR) from an ECDA is important for pipeline operators to promote safety while keeping operating cost at a minimum. Furthermore, with better estimates, the uncertainty from the DCVG indication is reduced and the decisions made on the locations of excavation is better informed. However, ensuring the accuracy of these estimates does not come without challenges. These challenges include (1) the need of proper methods for large data analysis from indirect assessment and (2) uncertainty about the probability distribution of quantities. Standard mean regression models e.g. the OLS, were used but fail to take the skewness of the distributions involved into account. The aim of this thesis is thus, to come up with statistical models to better predict TCDA and to interpret the %IR from the indirect assessment of an ECDA more precisely. The pipeline data used for the analyses is based on a recent ECDA project conducted by TWI Ltd. for the Middle Eastern Oil Company (MEOC). To address the challenges highlighted above, Quantile Regression (QR) was used to comprehensively characterise the underlying distribution of the dependent variable. This can be effective for example, when determining the different effect of contributing variables towards different sizes of TCDA (different quantiles). Another useful advantage is that the technique is robust to outliers due to its reliance on absolute errors. With the traditional mean regression, the effect of contributing variables towards other quantiles of the dependent variable is ignored. Furthermore, the OLS involves the squaring of errors which makes it less robust to outliers. Other forms of QR such as the Bayesian Quantile Regression (BQR) which has the advantage of supplementing future inspection projects with prior data and the Logistic Quantile Regression (LQR) which ensures the prediction of the dependent variable is within its specified bounds was applied to the MEOC dataset. The novelty of research lies in the approaches (methods) taken by the author in producing the models highlighted above. The summary of such novelty includes: * The use of non-linear Quantile Regression (QR) with interacting variables for TCDA prediction. * The application of a regularisation procedure (LASSO) for the generalisation of the TCDA prediction model.* The usage of the Bayesian Quantile Regression (BQR) technique to estimate the %IR and TCDA. * The use of Logistic Regression as a guideline towards the probability of excavation * And finally, the use of Logistic Quantile Regression (LQR) in ensuring the predicted values are within bounds for the prediction of the %IR and POPD. Novel findings from this thesis includes: * Some degree of relationship between the DCVG technique (%IR readings) and corrosion dimension. The results of the relationship between TCDA and POPD highlights a negative trend which further supports the idea that %IR has some relation to corrosion. * Based on the findings from Chapter 4, 5 and 6 suggests that corrosion activity rate is more prominent than the growth of TCDA at its median depth. It is therefore suggested that for this set of pipelines (those belonging to MEOC) repair of coating defects should be done before the coating defect has reached its median size. To the best of the Author's knowledge, the process of employing such approaches has never been applied before towards any ECDA data. The findings from this thesis also shed some light into the stochastic nature of the evolution of corrosion pits. This was not known before and is only made possible by the usage of the approaches highlighted above. The resulting models are also of novelty since no previous model has ever been developed based on the said methods. The contribution to knowledge from this research is therefore the greater understanding of relationship between variables stated above (TCDA, %IR and POPD). With this new knowledge, one has the potential to better prioritise location of excavation and better interpret DCVG indications. With the availability of ECDA data, it is also possible to predict the magnitude of corrosion activity by using the models developed in this thesis. Furthermore, the knowledge gained here has the potential to translate into cost saving measures for pipeline operators while ensuring safety is properly addressed.

Quantile Regression, as introduced by Koenker, R. and Bassett, G. (1978), provides a complete picture of the relationship between the response variable and covariates by estimating a family of conditional quantile functions. Also, it offers a natural solution to challenges such as; homoscedasticity and sometimes unrealistic normality assumption in the usual conditional mean regression. Most of the results for quantile regression are based on simple random sampling (SRS). In this thesis, we study the quantile regression with rank-based sampling methods. Rank-based sampling methods have a wide range of applications in medical, ecological and environmental research, and have been shown to perform better than SRS in estimating several population parameters. We propose a new objective function which takes into account the ranking information to estimate the unknown model parameters based on the maxima or minima nomination sampling designs. We compare the mean squared error of the proposed quantile regression estimates using maxima (or minima) nomination sampling design and observe that it provides higher relative e ciency when compared with its counterparts under SRS design for analyzing the upper (or lower) tails of the distribution of the response variable. We also evaluate the performance of our proposed methods when ranking is done with error. / February 2017

Thesis advisor: Zhijie Xiao / My dissertation research examines empirical issues in financial economics with a special focus on the application of quantile regression. This dissertation is composed by two self-contained papers, which center around: (1) robust estimation of conditional idiosyncratic volatility of asset returns to offer better understanding of market microstructure and asset pricing anomalies; (2) implementation of coherent risk measures in portfolio selection and financial risk management. The first chapter analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation errors in the presence of non-Gaussianity, casting strong doubt on the positive intertemporal idiosyncratic volatility effect reported in the literature. We propose an alternative estimator for conditional idiosyncratic volatility for GARCH-type models. The proposed estimation method does not require error distribution assumptions and is robust non-Gaussian innovations. Monte Carlo evidence indicates that the proposed estimator has much improved sampling performance over the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-section portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang and Zhang (2006) exists intertemporally, i.e., stocks with high conditional idiosyncratic volatility earn abnormally low returns. We solve the major piece of this puzzle by pointing out that previous empirical studies have failed to consider both idiosyncratic variance and individual conditional skewness in determining cross-sectional returns. We introduce a new concept - the "expected windfall" - as an alternative measure of conditional return skewness. After controlling for these two additional factors, cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges. The second chapter examines portfolio allocation decision for investors with general pessimistic preferences (GPP) regarding downside risk aversion and out-performing benchmark returns. I show that the expected utility of pessimistic investors can be robustly estimated within a quantile regression framework without assuming asset return distributions. The asymptotic properties of the optimal portfolio weights are derived. Empirically, this method is introduced to construct the optimal fund of CSFB/Tremont hedge-fund indices. Both the in-sample and out-of-sample backtesting results confirm that the optimal mean-GPP portfolio outperforms the mean-variance and mean-conditional VaR portfolios. / Thesis (PhD) — Boston College, 2009. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

This dissertation studies household income and consumption. In the first chapter, I identify the causal effect of retirement on health service utilization in China. In the second chapter, I investigates the impact that retirement has on the family support network of “sandwich” generations in China. In the third chapter, I propose a new estimator for linear quantile regression models with generated regressors, and apply it to study Engel curves for various commodity consumption for families in the UK. In the first chapter, I apply a regression discontinuity design by exploiting the exogenous mandatory retirement age rules in China in order to identify the causal effect of retirement on health service utilization. In China, the social insurance Urban Employee Basic Medical Insurance (UEBMI) provision continues after individuals retire. Employees, however, stop paying the premium and enjoy reduced cost sharing after they retire. Individual medical expenses, insurance costs, and benefits are recorded in the China Household Finance Survey 2013 (CHFS). Significantly, males and females respond differently to this decrease in the relative price of health insurance at the time of retirement. Females are generally more willing to increase their out-of-pocket expenditures in order to take advantage of better health insurance benefits and utilize more medical care. Males, by contrast, do not respond to this change in relative price in the same manner. In the second chapter, I investigates the impact that retirement has on the family support networks of “sandwich” generations in China. These middle-aged households have an inter-generational support network that includes both upward transfers (their parents or parents-in-law), as well as downward transfers (their children). I use micro data from CHARLS (China Health and Retirement Longitudinal Study) concerning middle-aged and elderly households in order to evaluate the changes that retirement can have on this family support network, primarily by exploiting the exogenous mandatory retirement age rules in China. I make the identifying assumption that inter-generational transfers would evolve more smoothly if households would not retire and apply a regression discontinuity approach. I find that retirement induces “sandwich” generations to switch roles in the private network as well as in the public transfer channel; indeed, is 55 percentage point more likely that households will switch from resource providers to resource recipients in the channel of private transfers. In addition, these “sandwich” generations are about 47 percentage point more likely to receive money from their non-coresident children when they retire. In the third chapter, we studies estimation and inference for linear quantile regression models with generated regressors. We suggest a practical two-step estimation procedure, where the generated regressors are computed in the first step. The asymptotic properties of the two-step estimator, namely, consistency and asymptotic normality are established. We show that the asymptotic variance-covariance matrix needs to be adjusted to account for the first-step estimation error. We propose a general estimator for the asymptotic variance-covariance, establish its consistency, and develop testing procedures for linear hypotheses in these models. Monte Carlo simulations to evaluate the finite-sample performance of the estimation and inference procedures are provided. Finally, we apply the proposed methods to study Engel curves for various commodities using data from the UK Family Expenditure Survey. We document strong heterogeneity in the estimated Engel curves along the conditional distribution of the budget share of each commodity. The empirical application also emphasizes that correctly estimating confidence intervals for the estimated Engel curves by the proposed estimator is of importance for inference.

Governments around the world have regulated multinational enterprises to adopt arm¡¦s length transactions to facilitate identifications and comparisons between non-transfer pricing transactions with independent, non-related enterprises and transactions with related enterprises that are suspected of transfer pricing. Currently, most of the optimal transfer pricing methods for establishing arm¡¦s length principles for multinational enterprises have been addressed in Organization for Economic Cooperation and Development's "Transfer Pricing Guidelines for Multinational Enterprises and Tax Administrations". These guidelines emphasize the establishment of a range of arm¡¦s length transactions through the comparability analysis and the economic analysis of transfer pricing transactions; a taxpayer's returns from transactions with related companies are then compared to the range of arm¡¦s length transactions. Currently the academic world is taking the initiative in the development of relevant models to describe corporate transfer pricing decisions or to measure the net income of corporate transfer pricing transactions. This research stems from these purposes and attempts to describe transfer pricing decisions in real practice through stringent modelling; this model is then used to measure the net income of transfer pricing transactions that took place among electronic industry participants who are publicly listed in the TSE or OTC in Taiwan. We further investigated the main factors that affect the levels of net income transferred by enterprises. Based on the empirical results of this research, we discovered that the impact of raw material costs is highly significant to the corporate transfer pricing decisions, and the magnitude of impacts vary depending on the allocation of net income from transfer pricing. We recommend that the tax administration detect corporate transfer pricing decisions by monitoring the weight of raw material costs in a company.

<p><strong>Background:</strong> Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study.</p><p><strong>Results:</strong> In this study, different normalization methods were tested, which are available in the R packages <em>Affy</em> and <em>qpcrNorm</em> for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that <em>qpcrNorm</em> Quantile normalization method performs best for all methods tested.</p><p><strong>Conclusions:</strong> The <em>qpcrNorm</em> Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.</p>

What motivates mergers in banking? The data show that merger activity is concentrated among very large banks. A large literature on the banking structure has studied this question by estimating cost functions and has provided mixed evidence. A crucial assumption is the exogeneity of input prices.If this assumption fails, result may be biased. This paper adopts the production function method proposed by Levinsohn and Petrin (2003) to separate the impact of productivity from scale economies in banking. To avoid this bias, I use recovery rates of non-performing loans, charge off rates, and cash holdings as proxies for productivity. The proxy method illustrates that the industry operates with significant diseconomies of scale, while the OLS method generates opposite results. Therefore, this finding supports the view that improvements in productivity cause mergers, which is also consistent with data. Finally, I introduce the Quantile Proxy Method to capture the impacts of both input endogeneity and size heterogeneity. This method reveals that medium size banks have largest diseconomies of scale, while top 5% experience somewhat extensive economies of scale. This result sheds light on the fact why many mergers occur among large banks: large parties involved in a consolidation benefit from both productivity improvements and scale economies. / text

Background: Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study. Results: In this study, different normalization methods were tested, which are available in the R packages Affy and qpcrNorm for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that qpcrNorm Quantile normalization method performs best for all methods tested. Conclusions: The qpcrNorm Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.

Changes in the temporal variability of precipitation at all timescales are expected due to global warming. Such changes affect urban water infrastructure by potentially influencing their performance and risk of failure. Unfortunately, there is considerable uncertainty about how hydrological variables will change in the future. While uncertainty is present at all timescales, the climate signal in the daily time series simulated by climate models, for instance, can be estimated with much greater certainty than in the simulated hourly time series. That is problematic as sub-daily precipitation time series are essential to solving specific water resource engineering problems, especially in urban hydrology, where times of concentrations are typically less than a day. For instance, hourly or sub-hourly precipitation time series are routinely used to design stormwater and road drainage systems. Rainfall variability at sub-daily time steps is often represented as Intensity-Duration-Frequency (IDF) curves, relating precipitation duration (of basin time of concentration) to return period and average precipitation intensity. Naturally, several researchers investigated the integration of climate change in IDF curves, leading to methods of variable complexity and variable performance. This thesis aims to a) make a critical analysis of the most commonly used methods for IDF curves under climate change in Canada and b) identify the methods with optimal performance for a set of stations located in the South Nation watershed in Ottawa, Ontario, and c) perform a case study highlighting the effect of the choice of the temporal disaggregation method on the estimated risk of failure/performance of an urban water system. The first part of the thesis examines Equidistant Quantile Mapping (EQM) used in the IDF_CC tool developed for the Canadian Water Network project. Two conceptual flaws in the method that led to a systematic underestimation of extreme events were discovered. Two corrections are proposed to the EQM, leading to the development of two new methods for IDF generation. The output of EQM and its improved version is a time series of annual maximum precipitation intensity for different durations that can be used to derive IDF curves. These time series generated using the above approach are not appropriate for rainfall-runoff models for which continuous time series of precipitation (not only maximums) are required. The second part of the thesis tackles the issue, which examines a different approach to evaluating the risk of failure/performance of urban water systems under a changing climate. This second approach yields continuous time series of precipitation that can be fed in rainfall-runoff models used for IDF curve generation. The proposed method is applied in three steps: i) projections of future daily precipitation are generated by downscaling the output of climate models; ii) the downscaled daily precipitation time series are temporally disaggregated to an hourly time step using various techniques; iii) finally, the disaggregated future precipitation time series are used as inputs to rainfall-runoff models or used to generate IDF curves. This approach relaxes several strong assumptions made to develop the EQM approach, such as the implicit (and strong) assumption that the annual maximum precipitation at two different time steps occurs during the same event. That assumption is not necessarily valid and can affect the realism of the generated IDF curves. The method's performance is obviously dependent on the temporal disaggregation technique used in step 3. In this thesis, a simple steady-state stochastic disaggregation model that generates wet/dry day occurrence using a binomial distribution and precipitation intensity using an exponential distribution is proposed and compared to widely used temporal disaggregation methods: the multiplicative random cascade model (MRC), the Hurst-Kolmogorov process (HKP), and three versions of the K-nearest neighbor model (KNN) using the nonparametric Kolmogorov- Smirnov (KS) test. The six disaggregation techniques were assessed at four stations located in South Nation River Watershed located in Eastern Ontario, Canada. The third part of the thesis is a case study of the impact of climate change on stormwater management. First, a stormwater management model (SWMM) of St. Catharines, Ontario, developed in a previous study, was selected to simulate its stormwater and sanitary system. The model was forced with downscaled and temporally disaggregated precipitation outputs of the Canadian Regional Climate Model at the Port Dalhousie station, simulated under emission scenario RCP8.5. The temporal disaggregation was done using the Fahad-Ousmane and the KNN (30) methods developed in the previous chapter. The impact of climate change on the frequency, volume, and quality of combined sewer overflows and other hydraulic parameters is examined. Results suggest an increase in the total volume, flow frequency percentage, maximum flow, and average flow in the stormwater system due to climate change. Therefore, adaptation measures should be implemented for the distribution network and wastewater treatment plant to convey and treat the wastewater resulting from wet and dry events.

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