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No description available.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 142-148).

The prospect of using the Internet and other Big Data methods to construct event data promises to transform the field but is stymied by the lack of a coherent strategy for addressing the problem of selection. Past studies have shown that event data have significant selection problems. In terms of conventional standards of representativeness, all event data have some unknown level of selection no matter how many sources are included. We summarize recent studies of news selection and outline a strategy for reducing the risks of possible selection bias, including techniques for generating multisource event inventories, estimating larger populations, and controlling for nonrandomness. These build on a relativistic strategy for addressing event selection and the recognition that no event data set can ever be declared completely free of selection bias.

Thesis (Ph.D.)--University of Alabama at Birmingham, 2008. / Title from PDF title page (viewed on Sept. 17, 2009). Includes bibliographical references (p. 93-97).

Microcredit programs are of great interest to economists and policymakers because of their potential for reducing poverty, particularly among women. The first chapter mainly investigates the effectiveness aspect of microcredit programs. Using program evaluation methods, we find significant improvement in women's economic condition after participating in these programs. This study also corrects for the self-selection bias that might arise due to the fact that women decide on whether to participate in the programs or not. The second chapter studies the determinants of women's economic performance in microcredit programs. These determinants are in the form of different types of characteristics of women: their own characteristics, such as age or schooling or the characteristics of the household or village they live in. One obstacle to measure the effect of observed characteristics is the problem of omitted variable bias, typically caused by unavailability of data on unobserved ability of individuals. In the absence of suitable instruments, this study finds information about unobserved ability from the marriage market. It is found that incorporating estimates of women's unobserved characteristics significantly changes the estimated effect of women's observed characteristics and substantially removes the omitted variable bias. Microcredit programs originated from Bangladesh and now three major microcredit programs are operating: Grameen Bank, BRAC and RD-12. The third chapter investigates how these different microcredit programs have been performing relative to each other. Using similar program evaluation technique as in chapter 1, we measure program impact on women's economic welfare for these programs separately. We find that BRAC outperforms Grameen Bank and RD-12 significantly. This result is interesting since it contradicts the popular notion that Grameen Bank is the most successful microcredit program. This study also tries to find the determinants of economic success of women participating in these programs, separately for each program. These results provide more insights into different aspects of microcredit program. / Ph. D.

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain disease. We show that random forest variable importance measures are a sensible means for variable selection in many applications, but are not reliable in situations where potential predictor variables vary in their scale level or their number of categories. This is particularly important in genomics and computational biology, where predictors often include variables of different types. Simulation studies are presented illustrating that, when random forest variable importance measures are used with data of varying types, the results are misleading because suboptimal predictor variables may be artificially preferred in variable selection. The two mechanisms underlying this deficiency are biased variable selection in the individual classification trees used to build the random forest on one hand, and effects induced by bootstrap sampling with replacement on the other hand. We propose to employ an alternative implementation of random forests, that provides unbiased variable selection in the individual classification trees. When this method is applied using subsampling without replacement, the resulting variable importance measures can be used reliably for variable selection even in situations where the potential predictor variables vary in their scale level or their number of categories. The usage of both random forest algorithms and their variable importance measures in the R system for statistical computing is illustrated and documented thoroughly in an application re-analysing data from a study on RNA editing. Therefore the suggested method can be applied straightforwardly by scientists in bioinformatics research. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

A loan deal is often composed of several components (for example, a 3-year revolving loan, a 10-year secured senior term loan, and a 5-year subordinated term loan). The division of a deal into two or more components, each with different risk characteristics, is called tranching. This study recognizes the importance of tranching and establishes tranching as an integral component of a syndicated loan structure. In the first essay, we present a model to explain the economic value of tranching and show that riskier firms are more likely to take loans with multiple tranches. Therefore, the average credit spread on syndicated loans with multiple tranches is higher than that on nontranched loans. However, after accounting for the risk characteristics of a tranched loan, we show that a given tranche of a multi-tranche loan, on average, has a lower credit spread than an otherwise similar loan that is not part of a multi-tranche loan. We also show that the benefits of tranching accrue primarily to borrowers with speculative debt ratings. Prior studies have found an abnormal stock return of 100 to 150 basis points for firms that announce they have borrowed funds from a bank. Despite some conflicting evidence (Peterson and Rajan, 2002; Thomas and Wang, 2004; Billett, Flannery and Garfinkel, 2006), the literature tends to interpret this positive bank loan announcement effect as the markets response to the mitigation of information asymmetry regarding the borrowing firm caused by the certification role of the lending banks who act as quasi-insiders. In the second essay, we document that a strong selection bias exists in prior studies. We show that less than a quarter of the loans made by banks are ever announced by borrowing firms and the loans that are announced are systematically different from loans that are never announced by the firms. Firms with low debt ratings, firms with zero or negative profits but positive interest expense, firms that take large loans in relation to their assets base, firms with little analyst following, and firms with high forecasted EPS growth are more likely to announce their loans. We show that while there was a positive announcement effect over the period 1987 to 1995, loan announcements elicited zero or negative returns in the last ten years as the mix of companies announcing loans changed over time.

Telemedicine offers an alternative referral strategy for fetal cardiology but is currently only used for ‘high-risk’ pregnancies. A case-study of a cost-consequences analysis comparing telemedicine to direct referral to a perinatal cardiologist is initially presented, which highlights that for high risk women for whom telemedicine was considered no cardiac anomalies were missed using either referral method. In the light of a review of the literature on the economics of telemedicine, three of the key methodological issues (of selection bias, of patient costs and using quality-adjusted life years (QALYs)) are explored to demonstrate how the case study analysis could be improved. Pregnant women were selected for referral based on their characteristics and risk factors; thus the cost and effects for the two groups may have been biased. Various methods identified in the literature are applied to the case study to reduce selection bias, but the analysis presented is unable to determine which method is best, given a number of limitations including the small sample size. The analysis is extended to include estimated total patient costs. However, when patient costs are added to the total costs of pregnancy, they did not substantially increase the overall cost. The results presented provide a guideline for future researchers and pregnant women of the likely costs during pregnancy. Given that the majority of missed cardiac anomalies were amongst low risk women, a decision analytical model is developed looking at the lifetime costs and QALYs of introducing telemedicine screening for pregnant women whose unborn babies are at a low risk of congenital heart disease. The analysis shows that offering telemedicine to all low risk women is the dominant strategy. The thesis demonstrates, within the constraints of existing data, that it would be cost-effective to provide telemedicine as part of an antenatal screening programme for all low risk women, and this would help prevent future ‘missed anomalies’.