Thesis advisor: Julie H. Mortimer / This dissertation investigates firms' strategic decisions in industries characterized by a retail sector and the subsequent welfare implications. The first chapter studies retailer assortment choices; the second investigates the effectiveness of retailer online advertising. In many industries producers reach consumers only through the retail sector. Retailer product assortment choices are crucial determinants of consumer welfare as well as retailers' and producers' profitability. Limited shelf space, an inherent characteristic of the brick-and-mortar retail sector, necessitates careful selection of product offerings. The assortment decision within a product category consists of two broad questions: "How many products to offer?" and "Which products to offer?". In sole-authored work, the first chapter focuses on the latter question and investigates the drivers and welfare consequences of retail product selections. While retailer assortment choices are primarily governed by consumers' preferences and retail sector competition, vertical contracts with producers may also influence product offerings, and, in turn, product availability in the market. From the producers' perspective, obtaining product distribution is imperative. Hence, producers frequently provide financial incentives to retailers to secure their patronage. These incentives often take the form of vendor allowances: lump-sum payments to retailers that do not directly depend on sales volume. They can take the form of slotting fees, warehousing allowances, cash discounts, allowances for damaged goods, or operating support (e.g. stocking personnel). Considering the spread of the retail sector, the impact of vertical contracts on product selections may substantially affect consumer welfare and firm profitability. Therefore, it is not surprising that vendor allowances have been the subject of policy discussion. Policy makers have raised concerns that these payments are harming disproportionately small producers and limiting consumer choice. Nevertheless, the Federal Trade Commission abstains from providing clear guidelines on the use of these payments due to unclear theoretical predictions and scarce empirical evidence. The main impediment to empirical analysis has been the proprietary nature of vertical contracts and firm costs. To overcome these data limitations, I develop a novel framework that allows me to quantify vendor allowances and analyze their effects on product selections and welfare. Using only data on retail prices, quantity sold, and retailer offerings, I estimate vendor allowances as retailers' opportunity cost of shelf space. Specifically, retailers face shelf-space limitations, hence, the opportunity cost of supplying a product is the sacrificed profits from not supplying a different product in its place. With limited assumptions on producer and retailer bargaining protocol, set estimates of vendor allowances are recovered. Additionally, by assuming that producers make take-it-or-leave-it offers, point estimates can be obtained. Lower bounds from set estimates imply that, on average, vendor allowances amount to at least 5% of retailer revenues. These results suggest that vendor allowances are likely important for retailer profitability, given that public grocery chains in the U.S. report profit margins on the order of 2-4% of revenues. To investigate the effects of these payments on product selections and welfare, I apply model estimates to simulate how market outcomes change in the absence of vendor allowances. The "what-if" experiment predicts that, absent vendor allowances, retailers fare worse, product variety is reduced as retailers replace "niche" products with "mainstream" options, but consumers are nevertheless better off. Small producers, which offer high-volume products, increase market distribution and profits, but, absent marginal cost data, consequences for large producers are uncertain. The work extends our understanding of how firms' strategic interactions in the marketplace may affect consumer welfare and firm profitability through product availability. The second chapter presents a coauthored work with Alexander Bleier and Maik Eisenbeiss that analyzes the use of online advertising personalization by an online retailer. Online advertising has become an important channel through which firms attempt to influence consumer behavior and increase sales. To improve effectiveness, firms today tailor their advertisements to individual consumers with a method called retargeting. In retargeting, firms track the shopping behaviors of individual consumers' visiting their online stores and, subsequently, deliver individualized display banner ads as consumers continue browsing the Web. While this method has gained traction in the online advertising industry, research in the field is still in its infancy. This work furthers our understanding of advertising personalization by analyzing two questions: How effective is ad personalization in attracting individual consumers back to the online store? And, do different personalization approaches have distinct impacts on consumers' engagement behaviors with the online store? To answer these research questions, we exploit unique data from a randomized field experiment conducted in cooperation with a major fashion and sporting goods retailer. This study compares the effects of online banners with very high, medium, and low degrees of content personalization. For example, very high personalization refers to ads showing consumers products that they had viewed at their previous visit to the retailer's online store. Medium personalization includes products from the most viewed category or brand of their previous visit. And low personalization delivers random products from the retailer's assortment without any connection to a consumer's previous shopping behavior. Results suggest that ads with very high personalization are more effective in bringing consumers back to the online store than the other campaigns. However, we also find that the gain in visits of very high- over medium-personalization banners stems mainly from visit with low consumer engagement. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Genus Danio includes zebrafish (Danio rerio) and several other phenotypically diverse species. To understand the history of these species and how they acquired the genetic differences underlying their diverse phenotypes, I performed two phylogenomic studies using Restriction-Site Associated DNA Sequencing and DNA hybridization-based exome enrichment. The results of these studies highlight important methodological considerations applicable to future experiments across taxa. Furthermore, these studies provide detailed understanding of the relationships within Danio including extensive introgression between lineages. The extent of introgression varies across the genome with regions of high recombination at the ends of chromosomes having the most evidence for introgression. Together, this work gives vital insight into the history of a model organism and the evolutionary processes that give rise to phenotypic diversity.

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.

Thesis advisor: Abhishek Chatterjee / Abstract Unnatural amino acid (UAA) incorporation is a powerful tool used by biochemists to discover the nature of protein structure and function. The evolution of orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs enables site-specific incorporation of UAAs proteins inside of living cells. The goal of this study was to further expand the repertoire of genetically encoded unnatural amino acids in E. coli as well as eukaryotes. We first attempted to engineer an aaRS, previously evolved for p-borono-phenylalanine (pBoF), to specifically charge 3-acetyl-p-borono-phenylalanine (AcpBoF). A randomized library of the pBoF-specific synthetases was generated and it was subjected to established selection schemes in a bacterial host. This report also describes the development of a yeast-based selection system to alter the substrate specificity of bacterial leucyl-tRNA synthetase, for genetic code expansion in eukaryotes. / Thesis (MS) — Boston College, 2017. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

在日常生活和科学研究中产生大量偏好数据，其反应一组被关注对象受偏好的程度。通常用排序数据或多元选择数据来记录观察结果。有时候关于两个对象的偏好没有明显强弱之分，导致排序产生节点，也就是所谓的删失排序。为了研究带有删失的排序数据，基于Thurstone的随机效用假设理论我们建立了一个对称贝叶斯probit模型。然而，参数识别是probit模型必须解决的问题，即确定一组潜在效用的位置和尺度。通常方法是选择其中一个对象为基，然后用其它对象的效用减去这个基的效用，最后我们关于这些效用差来建模。问题是，在用贝叶斯方法处理多元选择数据时，其预测结果对基的选择有敏感性，即选不同对象为基预测结果是不一样的。本文，我们虚构一个基，即一组对象偏好的平均。依靠这个基，我们为多元选择probit模型给出一个不依赖于对象标号的识别方法，即对称识别法。进一步，我们设计一种贝叶斯算法来估计这个模型。通过仿真研究和真实数据分析，我们发现这个贝叶斯probit模型被完全识别，而且消除通常识别法所存在的敏感性。接下来，我们把这个关于多元选择数据建立的probit模型推广到处理一般删失排序数据，即得到对称贝叶斯删失排序probit 模型。最后，我们用这个模型很好的分析了香港赌马数据。 / Vast amount of preference data arise from daily life or scientific research, where observations consist of preferences on a set of available objects. The observations are usually recorded by ranking data or multinomial data. Sometimes, there is not a clear preference between two objects, which will result in ranking data with ties, also called censored rank-ordered data. To study such kind of data, we develop a symmetric Bayesian probit model based on Thurstone's random utility (discriminal process) assumption. However, parameter identification is always an unavoidable problem for probit model, i.e., determining the location and scale of latent utilities. The standard identification method need to specify one of the utilities as a base, and then model the differences of the other utilities subtracted by the base. However, Bayesian predictions have been verified to be sensitive to specification of the base in the case of multinomial data. In this thesis, we set the average of the whole set of utilities as a base which is symmetric to any relabeling of objects. Based on this new base, we propose a symmetric identification approach to fully identify multinomial probit model. Furthermore, we design a Bayesian algorithm to fit that model. By simulation study and real data analysis, we find that this new probit model not only can be identifed well, but also remove sensitivities mentioned above. In what follows, we generalize this probit model to fit general censored rank-ordered data. Correspondingly, we get the symmetric Bayesian censored rank-ordered probit model. At last, we apply this model to analyze Hong Kong horse racing data successfully. / Detailed summary in vernacular field only. / Pan, Maolin. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 50-55). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.2 / Chapter 1.1.1 --- The Ranking Model --- p.2 / Chapter 1.1.2 --- Discrete Choice Model --- p.4 / Chapter 1.2 --- Methodology --- p.7 / Chapter 1.2.1 --- Data Augmentation --- p.8 / Chapter 1.2.2 --- Marginal Data Augmentation --- p.8 / Chapter 1.3 --- An Outline --- p.9 / Chapter 2 --- Bayesian Multinomial Probit Model Based On Symmetric I-denti cation --- p.11 / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- The MNP Model --- p.14 / Chapter 2.3 --- Symmetric Identification and Bayesian Analysis --- p.17 / Chapter 2.3.1 --- Symmetric Identification --- p.18 / Chapter 2.3.2 --- Bayesian Analysis --- p.21 / Chapter 2.4 --- Case Studies --- p.25 / Chapter 2.4.1 --- Simulation Study --- p.25 / Chapter 2.4.2 --- Clothes Detergent Purchases Data --- p.27 / Chapter 2.5 --- Summary --- p.29 / Chapter 3 --- Symmetric Bayesian Censored Rank-Ordered Probit Model --- p.30 / Chapter 3.1 --- Introduction --- p.30 / Chapter 3.2 --- Ranking Model --- p.33 / Chapter 3.2.1 --- Ranking Data --- p.33 / Chapter 3.2.2 --- Censored Rank-Ordered Probit Model --- p.35 / Chapter 3.2.3 --- Symmetrically Identified CROP Model --- p.36 / Chapter 3.3 --- Bayesian Analysis on Symmetrically Identified CROP Model --- p.37 / Chapter 3.3.1 --- Model Estimation --- p.38 / Chapter 3.4 --- Application: Hong Kong Horse Racing --- p.41 / Chapter 3.5 --- Summary --- p.44 / Chapter 4 --- Conclusion and Further Studies --- p.45 / Chapter A --- Prior for covariance matrix with trace augmented restriction --- p.47 / Chapter B --- Derivation of sampling intervals --- p.49 / Bibliography --- p.50

Digitized by Kansas Correctional Industries

Sexual selection can be defined as the difference in reproductive success among individuals that is related to their ability to acquire mates and fertilizations. Mathematical models have shown that combined with natural selection, sexual selection can be an important driver for speciation. There have also been a few case studies showing that sexual selection alone can lead to speciation in nature. Over the past 30 years, the barn swallow has become a classic model animal for sexual selection. The barn swallow has at least two sexually selected traits in different subspecies: the length of tail streamers in European barn swallows, H. r. rustica and ventral plumage colour in north American barn swallows, H. r. erythrogaster. Meanwhile molecular research on barn swallows shows that these barn swallow subspecies are recently derived and thus the barn swallow becomes an ideal model animal to test the theory that sexual selection drives speciation. The Barn Swallow species complex is comprised of six closely related subspecies distributed throughout the Holarctic. Whereas experimental studies in Europe, the Middle East and North America have been conducted, little is known about populations distributed across Asia. During my PhD study I collected barn swallow samples from more than 20 locations across China and compared the difference in morphology of Chinese barn swallow populations. The results show that in the northeastern part of China the barn swallow is intermediate in phenotype between subspecies in North America (H. r. erythrogaster) and subspecies in Europe (H. r. rustica), and is characterized by rusty ventral plumage colour and medium length tail streamers. Using morphological and colour traits, northeastern Chinese swallow populations can be separated from other Chinese populations, and this pattern of phenotypic variation may form under both natural and sexual selection. I also conducted an observational and experimental study on one of these populations, aiming to determine the extent to which variation in plumage color and tail streamers is underlain by sexual selection. The observational study reveals that for male barn swallows in my study population, clutch initiation date, the number of both social and genetic offspring and the body condition of nestlings can be predicted by the colouration of their ventral plumage, while the experiment shows that the reproductive success of male barn swallows tended to increase with experimental enhancement on their plumage colouration. My research supports that the ventral plumage colour is the sexually selected trait in northeastern Chinese barn swallows, and further experiments are needed to clarify the effect of male ventral plumage colour manipulation on their breeding success with the limitation of small sample size in my study.

Condition-dependent sexual selection has the potential to align natural and sexual selection and accelerate adaptation. When the expression of a sexually selected trait is constrained by the condition of the bearer, it offers a reliable signal of quality on which females can base mate choice decisions. Individuals with highly expressed sexually selected traits are therefore expected to possess advantageous genes given the prevailing environmental conditions. Such genes can then spread by their naturally selected benefits as well as their sexually selected advantages, thereby accelerating adaptation. I investigate the effect of condition-dependent traits on mating and signalling behaviour to explore the potential for alignment between natural and sexual selection in the wild. By studying a wild population of the field cricket, Gryllus campestris, I explore condition-dependent sexual selection in a natural context. This avoids some limitations of laboratory studies, which may overestimate effects in the absence of natural and environmental variation. I employ a direct experimental test of the effect of condition on sexually selected traits and mating success, finding that while male acoustic signals are condition-dependent, modest increases in calling effort do not result in increased mating success. I investigate the effect of body size on mating success and find mating success to be independent of body size. I explore the possibility of condition- and context-mediated flexibility in mate-searching tactics, finding that while population density influences tactic choice, individual condition is unlikely to predict which tactic a male will adopt. Finally, I consider the role of female condition in sexual selection and find that mating latency in females is not condition-dependent, but that mating history has an important effect on female choosiness.

The management of genomic diversity is important within breeding programs and is primarily achieved through controlling the rate of inbreeding. A failure to adequately manage the rate of inbreeding will result in an increased risk of the expression of lethal recessive mutations, inbreeding depression and losses in genetic variance, thereby restricting long-term genetic progress. Each research chapter within this thesis used real data collected from a commercial pig breeding operation to examine a key area of research regarding the management of genomic diversity. The first research chapter examined the selection outcomes from the practical application of Optimal Contributions (OC). These outcomes were examined to determine their alignment with the current theories regarding selection, particularly as to the extent by which selection decisions were influenced by estimated Mendelian sampling terms. This assessment was conducted for the initial selection of individuals as parents, which parents went on to provide a long-term contribution and the magnitude of these contributions. OC was shown to have shifted breeding decisions more closely in alignment with the estimated Mendelian sampling terms. The second research chapter used genomic data to assess the adequacy of the pedigree-based approach for managing diversity during selection. This approach assumes the infinitesimal model with all loci neutral and no impact from selection per se on heterozygosity. Using genomic information, the observed loss of heterozygosity at each marker was compared to the loss of heterozygosity expected from the pedigree-based relationships. Regional disparities between the observed and expected losses in heterozygosity were detected, which were potentially attributable to selection. Runs of homozygosity and the pairwise linkage disequilibrium between markers were also examined within these regions. Regions showing disparity were found to contain well validated quantitative trait loci for important traits. The third research chapter sought to provide a genomic solution to the shortcomings of the pedigree-based approach for quantifying relatedness, identified above. A methodology was devised for tracing identity by descent (IBD) at each allelic position over five ancestral generations, following phasing and imputation of the genomic data. A comparison was made between the inbreeding expected from the pedigree relationships and that observed from the identity by descent of genomic information. In the population studied it was not currently feasible to derive a relationship matrix based exclusively on observed IBD. The fourth research chapter used imputed genomic information to identify haplotypes which had a putative lethal recessive effect. Haplotypes which were never observed in the homozygous form, either in the population or in the offspring produced between carriers, were classified as candidate haplotypes. The top candidates on each chromosome were then examined for a reduction in the total number born when two carriers were mated together. A total of six putative lethal recessive haplotypes were detected relating to at least four putative lethal recessive mutations, where one homozygote was absent and the size of the reduction in litter size matched that expected for a lethal recessive effect. The research chapters contained within this thesis demonstrate the important role that genomics can have in managing inbreeding in addition to generating genetic gain. Genomics is able to provide a more accurate prediction of the Mendelian sampling term, better quantify the relatedness between individuals and detect lethal recessive effects.

Genomic evaluation techniques have been a huge success in the dairy cattle industry, as they allow accurate enough estimation of breeding values at a young age to allow selection decisions to be made at an earlier stage, thereby increasing the rate of genetic progress per annum. The success of genomic selection techniques relies on the existence of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL) across the population of interest; LD persists across larger distances within breeds than across breeds. Therefore, most success so far has been for selection within breeds, but the industry is keen for “across breed” evaluations to be developed, both in a multi-breed scenario which would allow evaluations for breeds that are numerically too small to carry out evaluations within breeds, and also for the evaluation of crossbred animals. This thesis investigates the potential for applying genomic selection techniques in both the multi-breed and crossbred scenarios. Chapter 2 examines the potential for a multi-breed reference population to improve the accuracy of genomic evaluation for a numerically small breed, for a range of production and non-production traits. The results provide evidence that forming a multi-breed reference population for two closely related breeds (Holstein and Friesian) results in a higher accuracy of GEBVs for the smaller breed, particularly when more phenotypic records are added via the single-step GBLUP method, and when a higher density SNP chip is used. Chapter 3 examines the crossbred scenario, whereby GEBVs are calculated for crossbred individuals based on a crossbred reference population. The population used for analysis was a highly crossbred African population, and GEBVs were calculated for three groups of animals chosen according to whether they had a high or low proportion of imported dairy genetics. Accuracy of prediction was higher than expected, and provided proof of concept for applying genomic selection techniques in crossbred African cattle populations. Chapter 4 investigates the potential for using novel SNPs derived from sequence data in order to estimate genomic relationships across cattle breeds, deploying data from two closely related breeds, Fleckvieh and Simmental, and a further distant European breed, the Brown Swiss. Novel SNPs were selected from sequence based on their putative impact on the genome, with impacts being inferred by SNP annotation software snpEff. Results showed that genomic relationships calculated using novel SNPs have a high correlation with genomic relationships calculated using SNPs common to the Illumina BovineHD SNP chip, though between-breed correlations were lower than those within breeds. The results presented in this thesis demonstrate that utilising a multi-breed reference population can improve the accuracy of prediction for a numerically small breed, and that genomic prediction of highly crossbred individuals is also feasible. However, differences between breeds and also types of crossbred animal suggest that no one solution can be used for all across-breed evaluations, and further research will be needed to allow commercial implementation in further populations.