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Access to abstract restricted until 05/2016. / Access to thesis restricted until 05/2016. / Department of Psychological Science

This thesis critically examines and assesses how Talent Identification and Development (TID) programmes for tennis are organised and implemented in four European countries. The thesis is based upon a multi-disciplinary and comparative research design, using both quantitative and qualitative research strategies, and the research methods of literature surveys, documentary research and semi-structured interviews, supplemented by a self-completion questionnaire. The study directly compares the views of key actors (players, coaches, administrators and parents) involved in TID practice in tennis in two of the countries: the Czech Republic and Germany. Two additional European countries, the United Kingdom (but focussing on England) and France, also form part of the research for comparative purposes. Following an original analysis of published and unpublished national and international literature, websites, documents and data in each of these countries, interviews were conducted in English and German with 39 key informants from the Czech Republic and Germany. The results indicated gaps between the theory and practice of TID and that tennis in the countries examined is likely to remain and become an even more socially exclusive sport within the next few years. Talent Development will be funded either by the public sport system, by private initiative, or both, but talent identification and development in tennis will remain limited to children from families with above average financial backgrounds.

This research examined the influence of eyewitness identification decisions on participants in the role of police investigators. Undergraduate "investigators" interviewed confederate "witnesses" and then searched a computer database of potential suspects. The database included information on each suspect's physical description, prior criminal record, alibi, and fingerprints. Participants selected a suspect and estimated the probability that the suspect was guilty. Investigators subsequently administered a photo lineup to the witness and re-estimated the suspect's guilt. Investigators were greatly swayed by eyewitness decisions. If the witness identified the suspect probability estimates increased dramatically. If the witness identified an innocent lineup member or rejected the lineup,+ investigators' probability estimates dropped significantly, even when pre-lineup objective evidence (e.g., fingerprints) was strong. Eyewitness decisions similarly influenced investigators' confidence in the witness and willingness to arrest the suspect. Participant-investigators greatly overestimated the amount of information gain provided by eyewitness identifications.

The use of biometrics is an evolving component in today's society. Fingerprint recognition continues to be one of the most widely used biometric systems. This thesis explores the various steps present in a fingerprint recognition system. The study develops a working algorithm to extract fingerprint minutiae from an input fingerprint image. This stage incorporates a variety of image pre-processing steps necessary for accurate minutiae extraction and includes two different methods of ridge thinning. Next, it implements a procedure for matching sets of minutiae data. This process goes through all possible alignments of the datasets and returns the matching score for the best possible alignment. Finally, it conducts a series of matching experiments to compare the performance of the two different thinning methods considered. Results show that thinning by the central line method produces better False Non-match Rates and False Match Rates than those obtained through thinning by the block filter method. / US Navy (USN) author.

Thesis advisor: Arthur Lewbel / My dissertation is composed of three chapters. The first chapter is on the asymptotic trimming and rate adaptive inference for heavy-tail distributed estimators. The second chapter is about the identification of the Average Treatment Effect for a two threshold model. The last chapter is on the identification of the parameters of interest in a binary choice model with interactive effects. / Thesis (PhD) — Boston College, 2015. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

A number of fungal species, particularly Candida species, are opportunistic pathogens capable of causing disease in humans. These range from relatively mild infections in healthy individuals (e.g. thrush) to life threatening systemic infections and colonisation of major organs in immunocompromised individuals. Existing methods of identifying Candida species in clinical samples are time and resource intensive and are not always specific enough to differentiate between drug-susceptible and drug-resistant species. Atlas Genetics have developed a novel electrochemical assay for the detection of nucleic acid from target pathogens using PCR followed by hybridisation by labelled probes. The work describes the development of a suite of probes and optimisation of reaction conditions for the rapid detection of fungal pathogens using this assay. A suite of five species-specific probes was developed to detect the five most clinically relevant Candida species as well as a pan-fungal probe capable of detection of DNA from any fungal species. Additionally, since the assay was novel, the work investigated other aspects such as probe and primer design which led to the development of a quick bioinformatics approach for design of species-specific probes which is also described. The results demonstrated that species-specific detection of C. albicans, C. glabrata, C. parapsilosis, C. tropicalis and C. krusei was possible, and that there was no cross reactivity exhibited by any probe with isolates from a test panel of other Candida species. The limit of detection of the assay was shown to be approximately one genome in 1ml of blood or 10 whole cells in 1ml of blood. The use of solid-state electrodes for detection provides an opportunity for miniaturisation of the assay into a robust, easily operated, portable system capable of rapid detection of pathogenic DNA in clinical samples either at point of care or in the microbiological laboratory.

Typescript. / Digitized by Kansas Correctional Industries

My dissertation consists of three chapters that concern identification in microeconometrics. The first two chapters discuss partial identification of distributional treatment effects in the causal inference models. The third chapter, which is joint work with Pierre-Andre Chiappori, studies identification of structural parameters in collective consumption models in labor economics. In the first chapter, I consider partial identification of the distribution of treatment effects when the marginal distributions of potential outcomes are fixed and restrictions are imposed on the support of potential outcomes. Examples of such support restrictions include monotone treatment response, concave or convex treatment response, and the Roy model of self-selection. Establishing informative bounds on the DTE is difficult because it involves constrained optimization over the space of joint distributions. I formulate the problem as an optimal transportation linear program and develop a new dual representation to characterize the general identification region with respect to the marginal distributions. I use this result to derive informative bounds for economic examples. I also propose an estimation procedure and illustrate the usefulness of my approach in the context of an empirical analysis of the effects of smoking on infant birth weight. The empirical results show that monotone treatment response has substantial identifying power for the DTE when the marginal distributions are given. In the second chapter, I study partial identification of distributional parameters in nonparametric triangular systems. The model consists of an outcome equation and a selection equation. It allows for general unobserved heterogeneity and selection on unobservables. The distributional parameters that I consider are the marginal distributions of potential outcomes, their joint distribution, and the distribution of treatment effects. I explore different types of plausible restrictions to tighten existing bounds on these parameters. My identification applies to the whole population without a full support condition on instrumental variables and does not rely on parametric specifications or rank similarity. I also provide numerical examples to illustrate identifying power of each restriction. The third chapter is joint work with Pierre-Andre Chiappori. In it, we identify the heterogeneous sharing rule in collective models. In such models, agents have their own preferences, and make Pareto efficient decisions. The econometrician can observe the household's (aggregate) demand, but not individual consumptions. We consider identification of `cross sectional' collective models, in which prices are constant over the sample. We allow for unobserved heterogeneity in the sharing rule and measurement errors in the household demand of each good. We show that nonparametric identification obtains except for particular cases (typically, when some of the individual Engel curves are linear). The existence of two exclusive goods is sufficient to identify the sharing rule, irrespective of the total number of commodities.