Strategic transit service planning in the Santurce/Old San Juan corridor

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.Includes bibliographical references (p. 205-208).by Elton K.L. Lin.S.M

Statistical Arbitrage Mining for Display Advertising

We study and formulate arbitrage in display advertising. Real-Time Bidding (RTB) mimics stock spot exchanges and utilises computers to algorithmically buy display ads per impression via a real-time auction. Despite the new automation, the ad markets are still informationally inefficient due to the heavily fragmented marketplaces. Two display impressions with similar or identical effectiveness (e.g., measured by conversion or click-through rates for a targeted audience) may sell for quite different prices at different market segments or pricing schemes. In this paper, we propose a novel data mining paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and exploiting price discrepancies between two pricing schemes. In essence, our SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per action)-based campaigns and CPM (cost per mille impressions)-based ad inventories; it statistically assesses the potential profit and cost for an incoming CPM bid request against a portfolio of CPA campaigns based on the estimated conversion rate, bid landscape and other statistics learned from historical data. In SAM, (i) functional optimisation is utilised to seek for optimal bidding to maximise the expected arbitrage net profit, and (ii) a portfolio-based risk management solution is leveraged to reallocate bid volume and budget across the set of campaigns to make a risk and return trade-off. We propose to jointly optimise both components in an EM fashion with high efficiency to help the meta-bidder successfully catch the transient statistical arbitrage opportunities in RTB. Both the offline experiments on a real-world large-scale dataset and online A/B tests on a commercial platform demonstrate the effectiveness of our proposed solution in exploiting arbitrage in various model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2015

Managing Risk of Bidding in Display Advertising

In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies

On the misidentification of species: sampling error in primates and other mammals using geometric morphometrics in more than 4,000 individuals

An accurate classification is the basis for research in biology. Morphometrics and morphospecies play an important role in modern taxonomy, with geometric morphometrics increasingly applied as a favourite analytical tool. Yet, really large samples are seldom available for modern species and even less common in palaeontology, where morphospecies are often identified, described and compared using just one or a very few specimens. The impact of sampling error and how large a sample must be to mitigate the inaccuracy are important questions for morphometrics and taxonomy. Using more than 4000 crania of adult mammals and taxa representing each of the four placental superorders, we assess the impacts of sampling error on estimates of species means, variances and covariances in Procrustes shape data using resampling experiments. In each group of closely related species (mostly congeneric), we found that a species can be identified fairly accurately even when means are based on relatively small samples, although errors are frequent with fewer specimens and primates more prone to inaccuracies. A precise reconstruction of similarity relationships, in contrast, sometimes requires very large samples (> 100), but this varies widely depending on the study group. Medium-sized samples are necessary to accurately estimate standard errors of mean shapes or intraspecific variance covariance structure, but in this case minimum sample sizes are broadly similar across all groups (≈ 20-50 individuals). Overall, thus, the minimum sample sized required for a study varies across taxa and depends on what is being assessed, but about 25-40 specimens (for each sex, if a species is sexually dimorphic) may be on average an adequate and attainable minimum sample size for estimating the most commonly used shape parameters. As expected, the best predictor of the effects of sampling error is the ratio of between- to within-species variation: the larger the ratio, the smaller the sample size needed to obtain the same level of accuracy. Even though ours is the largest study to date of the uncertainties in estimates of means, variances and covariances in geometric morphometrics, and despite its generally high congruence with previous analyses, we feel it would be premature to generalize. Clearly, there is no a priori answer for what minimum sample size is required for a particular study and no universal recipe to control for sampling error. Exploratory analyses using resampling experiments are thus desirable, easy to perform and yield powerful preliminary clues about the effect of sampling on parameter estimates in comparative studies of morphospecies, and in a variety of other morphometric applications in biology and medicine. Morphospecies descriptions are indeed a small piece of provisional evidence in a much more complex evolutionary puzzle. However, they are crucial in palaeontology, and provide important complimentary evidence in modern integrative taxonomy. Thus, if taxonomy provides the bricks for accurate research in biology, understanding the robustness of these bricks is the first fundamental step to build scientific knowledge on sound, stable and long-lasting foundations

The specialty choices of graduates from Brighton and Sussex Medical School: a longitudinal cohort study

BACKGROUND Since 2007 junior doctors in the UK have had to make major career decisions at a point when previously many had not yet chosen a specialty. This study examined when doctors in this new system make specialty choices, which factors influence choices, and whether doctors who choose a specialty they were interested in at medical school are more confident in their choice than those doctors whose interests change post-graduation. METHODS Two cohorts of students in their penultimate year at one medical school (n = 227/239) were asked which specialty interested them as a career. Two years later, 210/227 were sent a questionnaire measuring actual specialty chosen, confidence, influence of perceptions of the specialty and experiences on choice, satisfaction with medicine, personality, self-efficacy, and demographics. Medical school and post-graduation choices in the same category were deemed 'stable'. Predictors of stability, and of not having chosen a specialty, were calculated using bootstrapped logistic regression. Differences between specialties on questionnaire factors were analysed. RESULTS 50% responded (n = 105/277; 44% of the 239 Year 4 students). 65% specialty choices were 'stable'. Factors univariately associated with stability were specialty chosen, having enjoyed the specialty at medical school or since starting work, having first considered the specialty earlier. A regression found doctors who chose psychiatry were more likely to have changed choice than those who chose general practice. Confidence in the choice was not associated with stability. Those who chose general practice valued lifestyle factors. A psychiatry choice was associated with needing a job and using one's intellect to help others. The decision to choose surgical training tended to be made early. Not having applied for specialty training was associated with being lower on agreeableness and conscientiousness. CONCLUSION Medical school experiences are important in specialty choice but experiences post-graduation remain significant, particularly in some specialties (psychiatry in our sample). Career guidance is important at medical school and should be continued post-graduation, with senior clinicians supported in advising juniors. Careers advice in the first year post-graduation may be particularly important, especially for specialties which have difficulty recruiting or are poorly represented at medical school

An Exploratory Study Of Student Satisfaction With University Web Page Design

This exploratory study evaluates the satisfaction of students with a web-based information system at a medium-sized regional university. The analysis provides a process for simplifying data interpretation in captured student user feedback. Findings indicate that student classifications, as measured by demographic and other factors, determine satisfaction levels towards various web sources of information. Differences in satisfaction levels across student groups based on gender, age, minority status, employment, and current course load were found. Implications for university web designers and university administrators are considered and discussed

Data clustering and noise undressing for correlation matrices

We discuss a new approach to data clustering. We find that maximum likelihood leads naturally to an Hamiltonian of Potts variables which depends on the correlation matrix and whose low temperature behavior describes the correlation structure of the data. For random, uncorrelated data sets no correlation structure emerges. On the other hand for data sets with a built-in cluster structure, the method is able to detect and recover efficiently that structure. Finally we apply the method to financial time series, where the low temperature behavior reveals a non trivial clustering.Comment: 8 pages, 5 figures, completely rewritten and enlarged version of cond-mat/0003241. Submitted to Phys. Rev.