Thesis--University of Oklahoma. / Bibliography: leaves 62-63.

Thesis--University of Oklahoma. / Bibliography: leaves 82-83.

Broadly, this study focuses on roles of sell-side analysts and examines the determinants and consequences of information discovery and stock timing roles by sell-side analysts. We also re-examine reiterations of prior recommendations by sell-side analysts. In Chapter 1, the contribution is to document that analysts add value by engaging in discovery of private information and this value addition is greater than that due to interpretation of public news or stock timing. The innovation in this Chapter is to read over 3,700 analyst reports from Investext and explicitly identify whether the report contains discovery, interpretation, and/or timing. Analysts discover new information by talking to management sources (personal meetings, investor meetings, and conference calls) or non-management sources (such as channel checks). We find that information discovery is prevalent in 17% of the reports. The cumulative abnormal return (CAR) for reports containing discovery are 6.3% for upgrades and -10.6% for downgrades. The CARs are higher for reports containing discovery relative to those containing interpretation or timing. We find that economic determinants predict whether a report will contain discovery. Discovery from management sources is more likely for reports in the pre-Reg FD period and for reports by optimistic analysts. Discovery from non-management sources is more likely for reports written by All-Star analysts, and for firms that have high information asymmetry and those that are followed by more analysts. In Chapter 2, the contribution is to introduce and document a third role that analysts play that is also valuable to investors, which we term "stock timing." Specifically, we define a timing report as one where the analyst revises his recommendation but does not revise the Price Target or any of the 23 fundamental drivers of stock price (such as EPS, FCF) tracked by I/B/E/S. Because the analyst maintains the same price target as in his prior report but still revises his recommendation, such timing calls are contrarian valuation calls. Analysts issue timing downgrades (upgrades) in response to price increases (declines) since the release of their prior report on the firm. 30% of all revisions are timing reports, indicating the importance of the timing role played by analysts. If analysts have timing ability, then markets should react to the release of the timing report and we should observe that economic determinants explain the cross-sectional variation in timing ability. We find the 3-day announcement return is over 2% in magnitude, 62% of the reports are winners (have announcement returns that have the correct sign), 10% of the reports are large enough to be considered influential, and 37% of the reports are persistent winners. These results suggest that analysts have timing ability. The ability to time is similar is magnitude to information interpretation but smaller compared to information discovery. We find considerable cross-sectional and time-series variation in timing ability. We find that the probability of issuing a timing report is positively related to the opportunities to time the stock provided by potential mispricing. Conditional on issuing a timing report, the probability of issuing a winner, an influential winner, or a persistent winner is positively related to analyst experience and negatively related to the costs associated with issuing a timing report. In Chapter 3, we document that recommendation reiterations are not homogeneous and there is a large subset of reiterations that are as much valued by investors as recommendation revisions. We combine Detail History file containing the measures tracked by I/B/E/S (Price Target, EPS, etc.) and Recommendation file to create the full time series of recommendations (initiations, reiterations, and revisions) made by each analyst for each firm for 14 years from 1999 to 2012. By adopting a modified version of "filling in the holes" method, we find that recommendation reiterations are prevalent, consisting of about 80% of recommendations for our 14-year sample period. Second, market response to recommendation reiterations increases monotonically from Reiteration: Strong Sell to Reiteration: Strong Buy. Third, reiterations coupled with contemporary changes in price targets and/or earning forecasts bring substantial absolute abnormal stock returns to investors. Lastly, when we replicate what Loh and Stulz (2011), we find that the number of reiterations which are influential is more than twice that of recommendation revisions that are influential. / Business Administration/Finance

As sequencing technology continues to produce better quality genomes at decreasing costs, there has been a recent surge in the variety of data that we are now able to analyze. This is particularly true with regards to our understanding of the human genome—where the last decade has seen data advances in primate epigenomics, ancient hominid genomics, and a proliferation of human polymorphism data from multiple populations. In order to utilize such data however, it has become critical to develop increasingly sophisticated tools spanning both bioinformatics and statistical inference. In population genetics particularly, new statistical approaches for analyzing population data are constantly being developed—unfortunately, often without proper model testing and evaluation of type-I and type-II error. Because the common Wright-Fisher assumptions underlying such models are generally violated in natural populations, this statistical testing is critical. Thus, my dissertation has two distinct but related themes: 1) evaluating methods of statistical inference in population genetics, and 2) utilizing these methods to analyze the evolutionary history of humans and our closest relatives. The resulting collection of work has not only provided important biological insights (including some of the first strong evidence of selection on human-specific epigenetic modifications (Shulha, Crisci, Reshetov, Tushir et al. 2012, PLoS Bio), and a characterization of human-specific genetic changes distinguishing modern humans from Neanderthals (Crisci et al. 2011, GBE)), but also important insights in to the performance of population genetic methodologies which will motivate the future development of improved approaches for statistical inference (Crisci et al, in review).

A novel data analysis approach that is automatic, self-learning and self-explained, and which provides accurate and reliable results is reported. The data analysis tool is capable of performing multivariate non-parametric regression analysis, as well as quantitative inferential analysis using predictive learning. Statistical approaches such as multiple regression or discriminant analysis are usually used to perform this kind of analysis. However, they lack universal capabilities and their success in any particular application is directly affected by the problem complexity.The approach employs the use of Artificial Neural Networks (ANNs) and Fuzzy Logic to perform the data analysis. The features of these two techniques are the means by which the developed data analysis approach has the ability to perform self-learning as well as allowing user interaction in the learning process. Further, they offer a means by which rules may be generated to assist human understanding of the learned analysis model, and so enable an analyst to include external knowledge.Two problems in the resource industry have been used to illustrate the proposed method, as these applications contain non-linearity in the data that is unknown and difficult to derive. They are well log data analysis in petroleum exploration and hydrocyclone data analysis in mineral processing. This research also explores how this proposed data analysis approach could enhance the analysis process for problems of this type.

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Older patients suffer from a greater number of medical morbidities, consume a greater number of prescribed medications, and report lower levels of quality of life than their younger counterparts. The objectives of this study were to determine whether there is 1) an association between medical morbidity and symptom burden or 2) an association between medication use and symptom burden. This was a cross-sectional study of the symptoms, medical morbidities, and medications reported by 159 community-dwelling male patients 65 years of age or older. Correlations were drawn using linear regression analysis. On average, the participants in this study suffered from 2.56 +/- 1.36 medical morbidities, were prescribed 7.91+/- 2.83 medications, and reported 3.17 symptoms at any severity. The results of this study demonstrated a direct correlation between number of medical morbidities and symptom burden (R2 = 0.94). Our study did not find a significant correlation between medication use and symptom burden (R2 = 0.20). The findings of this study suggest that the number of medical morbidities has a stronger negative impact on symptom burden than the number of medications used. Thus, when attempting to improve quality of life for older patients, physicians should focus on the treatment and alleviations of symptoms associated with medical morbidity.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 133-141).

Diss. (sammanfattning) Umeå : Univ., 2008. / Härtill 5 uppsatser.

Thesis (Ph.D. in Biostatistics) -- University of Colorado Denver, 2007. / Typescript. Includes bibliographical references (leaves 70-71). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;