Texte remanié de: Th. doct.--Droit public--Paris 11, 1996. / Bibliogr. p. 357-366. Index.

<p>Stochastic volatility models have been focus for research in recent years.</p><p>One interesting and important topic has been the estimation procedure.</p><p>For a given stochastic volatility model this project aims to compare two</p><p>methods of parameter estimation.</p>

Stochastic volatility models have been focus for research in recent years. One interesting and important topic has been the estimation procedure. For a given stochastic volatility model this project aims to compare two methods of parameter estimation.

Awareness techniques have been proposed and studied to aid developer understanding, efficiency, and quality of software produced. Some of these techniques have focused on either direct or indirect conflicts in order to prevent, detect, or resolve these conflicts as they arise from a result of source code changes. While the techniques and tools for direct conflicts have had large success, tools either proposed or studied for indirect conflicts have had common issues of information overload, false positives, scalability, information distribution and many others. To better understand these issues, this dissertation will focus on exploring the world of indirect conflicts through 4 studies. The first two studies presented will focus on motivational circumstances which occur during the software development life cycle and cause indirect conflicts. Developers interactions are studied in order to create a tool which can aid in the workflows around indirect conflicts. The second two studies present a deeper investigation into why most indirect conflict tools fail to attract developer interest through exploring the root causes of indirect conflicts and how tools should be properly built to support developer workflows. / Graduate / 0984

Awareness techniques have been proposed and studied to aid developer understanding, efficiency, and quality of software produced. Some of these techniques have focused on either direct or indirect conflicts in order to prevent, detect, or resolve these conflicts as they arise from a result of source code changes. While the techniques and tools for direct conflicts have had large success, tools either proposed or studied for indirect conflicts have had common issues of information overload, false positives, scalability, information distribution and many others. To better understand these issues, this dissertation will focus on exploring the world of indirect conflicts through 4 studies. The first two studies presented will focus on motivational circumstances which occur during the software development life cycle and cause indirect conflicts. Developers interactions are studied in order to create a tool which can aid in the workflows around indirect conflicts. The second two studies present a deeper investigation into why most indirect conflict tools fail to attract developer interest through exploring the root causes of indirect conflicts and how tools should be properly built to support developer workflows. / Graduate / 0984

While many clinical practice guidelines that use the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach have ample published evidence to support their recommendations, certain situations (e.g. rare, rapidly emerging, or understudied diseases) may be limiting. As a result, it is common for guideline developers to seek out indirect evidence from other areas to fill in these evidence gaps. The GRADE evidence-to-decision (EtD) framework, which offers a structured and transparent development process for guidelines, includes additional research evidence domains (e.g. feasibility, acceptability, equity) for panels to consider in their decision-making process. This confluence of both considerations of indirect information and increasing literature domain sources to draw from when making decisions weighs on the simplicity of literature presentation. Herein, firstly, we described an example of specific decision-rules for including indirect evidence and the implications of the rules for presenting results to decision-makers. Secondly, we provided a comprehensive overview of how guideline developers currently report economic information across GRADE evidence-to-decision frameworks. Lastly, we ranked the most important study characteristics suggested in the literature by economists to consider as decision-rules when assessing indirectness (transferability) of economic evaluations chosen as research evidence in a GRADE guideline. We conclude that developers, with the help of their panels, should work to establish and report clear decision-rules and the rationale for indirect evidence that they select for their clinical practice guidelines. This has the potential to simplify the presentation of indirect evidence for panels and developers, as well as, to reduce decision-making confusion, time demands and guideline funder costs. / Thesis / Candidate in Philosophy / While many clinical practice guidelines have ample published evidence to support their recommendations, certain situations (e.g. rare, rapidly emerging, or understudied diseases) may be limiting the available literature. As a result, it is common for guideline developers to seek out indirect evidence from other, but related areas, to fill in these evidence gaps. Selection of available indirect evidence may be better than basing decisions on no evidence, in particular in situations of clinical equipoise. However, including all potentially relevant indirect evidence may represent an overuse of evidence. Indirect evidence refers to information sources with related populations, interventions, outcomes or comparisons, which could reasonably be extrapolated; but, are not entirely specific to the research topic at hand. This confluence of both indirect information considerations and desire by many for increasing literature sources to draw from weighs on the simplicity of an overall summary of literature presented during a guideline recommendation decision-making process. Herein, firstly, we described an example of explicit decision-rules for including indirect evidence that were specific and the implications of the rules for presenting results to decision-makers. Secondly, we provided a comprehensive overview of how guideline developers currently report economic information across guideline frameworks, in particular with respect to indirectness. Lastly, we described the most important study characteristics suggested by economists to consider as decision-rules when assessing economic evaluations for use as research evidence in a guideline. This work presents important concepts for guideline developers to consider when choosing indirect evidence sources in their clinical practice guidelines. Our findings have the potential to simplify the presentation of indirect evidence for guideline panels and developers, as well as, to reduce decision-making confusion, time demands and guideline funder costs.

The purpose of this study was to investigate the perceptions that college age women have of social aggression and its consequences in their lives. Qualitative research methods were used to analyze written narrative responses to a question posed to women enrolled in a human sexuality class at Virginia Tech. Although 83 narrative responses were selected, 32 narratives that met criteria were examined using modified analytic induction. A coding scheme was devised and the codes were applied to each narrative and revised many times. The findings of the study suggest that the consequences of social aggression continue for months and even years after incident has occurred. Women indicated that their relationships have changed due to their experiences and that these experiences have made them cautious of friendships with women. Furthermore, many women acknowledged that they perceive men to be more trustworthy and better friends than women because of the "mean" nature of women. Additionally, women had a difficult time acknowledging their own meanness and attempted to justify meanness that they did acknowledge. Further research is needed to explain why women feel they cannot trust other women. Additionally, more research is needed to explain why women perceive men to be more trustworthy and why they perceive that men are better friends when previous research suggests that social aggression levels even out during late adolescence and emerging adulthood. / Master of Science

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No description available.

Reducing disease prevalence through selection for host resistance offers a desirable alternative to chemical treatment which is a potential environmental concern due to run-off, and sometimes only offers limited protection due to pathogen resistance for example (Chen et al., 2010). Genetic analyses require large sample sizes and hence disease phenotypes often need to be obtained from field data. Disease data from field studies is often binary, indicating whether an individual became infected or not following exposure to infectious pathogens. In genetic analyses of binary disease data, however, exposure is often considered as an environmental constant and thus potential variation in host infectivity is ignored. Host infectivity is the propensity of an infected individual to infect others. The lack of attention to genetic variation in infectivity stands in contrast to its important role in epidemiology. The theory of indirect genetic effects (IGE), also known as associative or social genetic effects, provides a promising framework to account for genetic variation in infectivity as it investigates heritable effects of an individual on the trait value of another individual. Chapter 2 examines to what extent genetic variance in infectivity/susceptibility is captured by a conventional model versus an IGE model. The results show that, unlike a conventional model, which does not capture the variation in infectivity when it is present in the data, a model which takes IGEs into account captures some, though not all, of the inherent genetic variation in infectivity. The results also show that genetic evaluations that incorporate variation in infectivity can increase response to selection and reduce future disease risk. However, the results of this study also reveal severe shortcomings in using the standard IGE model to estimate genetic variance in infectivity caused by ignoring dynamic aspects of disease transmission. Chapter 3 explores to what extent the standard IGE model could be adapted for use with binary infectious disease data taking account of dynamic properties within the remit of a conventional quantitative genetics mixed model framework and software. The effect of including disease dynamics in this way was assessed by comparing the accuracy, bias and impact for estimates obtained for simulated binary disease data with two such adjusted IGE models, with the Standard IGE model. In the first adjusted model, the Case model, it was assumed that only infected individuals have an indirect effect on their group mates. In the second adjusted IGE model, the Case-ordered model, it was assumed that only infected individuals exert an indirect effect on susceptible group mates only. The results show that taking the disease status of individuals into account, by using the Case model, considerably improves the bias, accuracy and impact of genetic infectivity estimates from binary disease data compared to the Standard IGE model. However, although heuristically one would assume that the Case-ordered model would provide the best estimates, as it takes the disease dynamics into account, in fact it provides the worst. Moreover, the results suggest that further improvements would be necessary in order to achieve sufficiently reliable infectivity estimates, and point to inadequacy of the statistical model. In order to derive an appropriate relationship between the observed binary disease trait and underlying susceptibility and infectivity, epidemiological theory was combined with quantitative genetics theory to expand the existing framework in Chapter 4. This involved the derivation of a genetic-epidemiological function which takes dynamic expression of susceptibility and infectivity into account. When used to predict the outcome of simulated data it proved to be a good fit for the probability of an individual to become infected given its own susceptibility and the infectivity of its group mates. Using the derived function it was demonstrated that the use of a linear IGE model would result in biased estimates of susceptibility and infectivity as observed in Chapters 2 & 3. Following the results of Chapter 4, the derived expression was used to develop a Markov Chain Monte Carlo (MCMC) algorithm in order to estimate breeding values in susceptibility and infectivity in Chapter 5. The MCMC algorithm was evaluated with simulated disease data. Prior to implementing this algorithm with real disease data an adequate experimental design must be determined. The results suggest that there is a trade-off for the ability to estimate susceptibility and infectivity with regards to group size; this is in line with findings for IGE models. A possible compromise would be to place relatives in both larger and smaller groups. The general discussion addresses such questions regarding experimental design and possible areas for improvement of the algorithm. In conclusion, the thesis advances and develops a novel approach to the analysis of binary infectious disease data, which makes it possible to capture genetic variation in both host susceptibility and infectivity. This approach has been refined to make those estimates increasingly accurate. These breeding values will provide novel opportunities for genome wide association studies and may lead to novel genetic disease control strategies tackling not only host resistance but also the ability to transmit infectious agents.