Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

It has been demonstrated that people often feel happier, healthier, and more relaxed after a vacation. However, there is still lack of research on how people perceive the benefits of travel and how these perceptions influence their travel behavior. Thus, the primary purpose of this research was to examine the effects of perceived tourism benefits on travel behavior based on the model of attitude importance. Since existing scales of tourism benefits failed to incorporate some important items or factors, particularly the health benefits of tourism, this dissertation involved three online panel surveys, including: (1) a preliminary study (n=566) to elicit new benefit items, (2) a pilot study (n=434) to trim down the number of items, and (3) a main survey (n=559) to finalize the scale. As a result, several items associated with health benefits were elicited from the preliminary study; in the later stages of scale development, these items were identified and validated as a convergent dimension of perceived health benefits. Further, several hypotheses pertaining to the effect of perceived tourism benefits and the applicability of the attitude-importance model in tourism were tested. The results showed that: (1) the premise of the attitude-importance model that important attitudes can instigate the process of knowledge accumulation was supported; (2) the applicability of the attitude-importance model in tourism was supported; (3) the three factors of perceived tourism benefits – experiential, health, and relaxation benefits, had positive effects on travel behavior through attitude importance. These results had theoretical and practical implications. First, while previous tourism studies on tourists’ information search have tended to incorporate information search behavior in the context of vacation planning, this research demonstrated that the accumulation of product-related knowledge can be on a regular basis. Second, while previous tourism studies have a strong preference for the evaluative features of attitudes, this research demonstrated that attitude importance as a dimension of attitude strength is relevant in tourism. Finally, the experiential, health, and relaxation benefits were shown to have positive effects on travel behavior, which indicates that the tourism industry can encourage people to travel more by convincing them taking vacations is beneficial.

This research intends to understand whether Kaohsiung is heading toward or away from being a livable city. Additionally, it also intends to show if the developments in the city conform to the expectations of the residents. Therefore, the analysis in this research is based on objective statistics and the resident¡¦s subjective perceptions. This research utilized Time Series analysis and questionnaires to conduct the research, and used importance-performance analysis as the analytical method. The questionnaires targeted the residents in Kaohsiung City who are over 20 years of age. The total samples are 330 with 254 valid samples. The questionnaires surveyed the level of livability of Kaohsiung judging from 5 aspects: the eco-environment, culture & education, economic development, urban living & service, and medical & social welfare, reflecting the difference between the importance and performance of each aspect. Below are the suggestions concluded from the results of the research, which pointed out the improvements needed for Kaohsiung and the items that can use less attention: 1. According to the time aptitude objective statistics, Kaohsiung has shown mostly positive growth on cultural education, especially on holding cultural events and replenishing books for the public libraries. However, the economy has shown negative growth, 2. Based on importance-performance analysis, out of 23 indications, 4 of them (17.38%) fell on keep-doing area, 4(17.38%) fell on excessive supply area, 6(26.1%) fell on lower-priority area, 9(39.14%) fell on improvement-focused area. 3. Combing the data gathered from the questionnaires and statistical analysis, the items require grave improvement are raising the wages of the residents, lowering unemployment rate, and resolving the problem of abuse to children and teenagers. From both the subjective and objective analysis, items that are overly supplied are the number of times of holding cultural events and replenishing books for the pubic libraries. Based on the results from the research, it is suggested that Kaohsiung put resource to economy and medical & social welfare, while decrease overly investing cultural education.

The past few years have seen the rapid rise of all things “social” on the web from the growth of online social networks like Facebook, to real-time communication services like Twitter, to user-contributed content sites like Flickr and YouTube, to content aggregators like Digg. Beyond these popular Web 2.0 successes, the emer- gence of Social Information Systems is promising to fundamentally transform what information we encounter and digest, how businesses market and engage with their customers, how universities educate and train a new generation of researchers, how the government investigates terror networks, and even how political regimes interact with their citizenry. Users have moved from being passive consumers of information (via querying or browsing) to becoming active participants in the creation of data and knowledge artifacts, actively sorting, ranking, and annotating other users and artifacts. This fundamental shift to social systems places new demands on providing de- pendable capabilities for knowing whom to trust and what information to trust, given the open and unregulated nature of these systems. The emergence of large-scale user participation in Social Information Systems suggests the need for the development of user-centric approaches to information quality. As a step in this direction this research proposes an interaction-based approach for modeling the notion of user im- portance. The interaction-based model is centered around the uniquely social aspects of these systems, by treating who communicates with whom (an interaction) as a core building block in evaluating user importance. We first study the interaction characteristics of Twitter, one of the most buzzworthy recent Social Web successes, examining the usage statistics, growth patterns, and user interaction behavior of over 2 million participants on Twitter. We believe this is the first large-scale study of dynamic interactions on a real-world Social Information System. Based on the anal- ysis of the interaction structure of Twitter, the second contribution of this thesis research is an exploration of approaches for measuring user importance. As part of this exploration, we study several different approaches that build on the inherent interaction-based framework of Social Information Systems. We explore this model through an experimental study over an interaction graph consisting of 800,000 nodes and about 1.9 million interaction edges. The user importance modeling approaches that we present can be applied to any Social Information System in which interactions between users can be monitored.

The Republic of Moldova: The very existence of this country is not widely known around the world, especially in the United States. When mentioning the country in an international context, journalists and political leaders refer to Moldova’s status as Europe’s poorest country, its two-year struggle to elect a president, or its breakaway region of Transnistria. In a reemerging Russia, however, the Republic of Moldova is of considerable strategic importance. Because of Moldova’s geographic location and ethnic make-up, Russia has a genuine interest in ensuring that Moldova maintains the status quo and continues to be a poor, divided, and weak state. Based on the lack of a national identity, an absent economy, and a struggling government; it is questionable whether or not Moldova should be an independent state in the first place. This report will argue that despite attractive prospects for Moldova to unite with Romania or integrate into the European Union, Russia will ensure that it remains an independent, yet divided country. / text

The well-documented problems associated with mapping raw rates of disease have resulted in an increased use of Bayesian hierarchical models to produce maps of "smoothed'' estimates of disease rates. Two statistical problems arise in using Bayesian hierarchical models for disease mapping. The first problem is in comparing goodness of fit of various models, which can be used to test different hypotheses. The second problem is in identifying outliers/divergent regions with unusually high or low residual risk of disease, or those whose disease rates are not well fitted. The results of outlier detection may generate further hypotheses as to what additional covariates might be necessary for explaining the disease. Leave-one-out cross-validatory (LOOCV) model assessment has been used for these two problems. However, actual LOOCV is time-consuming. This thesis introduces two methods, namely iIS and iWAIC, for approximating LOOCV, using only Markov chain samples simulated from a posterior distribution based on a full data set. In iIS and iWAIC, we first integrate the latent variables without reference to holdout observation, then apply IS and WAIC approximations to the integrated predictive density and evaluation function. We apply iIS and iWAIC to two real data sets. Our empirical results show that iIS and iWAIC can provide significantly better estimation of LOOCV model assessment than existing methods including DIC, Importance Sampling, WAIC, posterior checking and Ghosting methods.

The aim of this research was to provide a practical method for assessing visitor satisfaction at a local festival. It is crucial for festival management to monitor and evaluate visitor satisfaction in order to understand and identify the needs and perceptions of attendees, which in turn allows organizers to design and tailor the festival elements towards them, leading to higher visitor satisfaction, positive word-of-mouth advertising, and increased likelihood of repeat attendance (Lee, Lee and Choi, 2011; Lee & Beeler, 2009). The research objectives were to evaluate current levels of satisfaction of festival attendees, to determine what attributes are importance in determining satisfaction, and to analyze whether importance and performance of those attributes differs based on demographics and visit characteristics, with the aim of recommending policies to assist the festival in increasing overall visitor satisfaction. A questionnaire was distributed over four days, and three event locations resulting in the collection of 389 completed questionnaires. Respondents were asked to complete demographic and visit information as well as rate the importance and performance of eighteen festival attributes. ANOVA and independent t-tests were used in order to determine whether the importance and satisfaction of these attributes differed based on the demographics and visit characteristics. An Importance-performance analysis (IPA) was then used to assist event organizers in resource allocation while identifying critical performance attributes in order to improve visitor satisfaction. Findings reveal attributes associated with program content, convenience and food and beverage ranked higher in determining visitor satisfaction than the attributes associated with souvenir, transportation and information availability. Results also indicate statistically significant differences of the mean importance and mean performance scores of attributes based on gender, age, resident status, site and whether it was the respondents??? first time at the event. It was found that females place a higher importance on convenience attributes such as the cleanliness of restrooms, helpfulness of staff and feeling of safety, as compared to males. As well, repeat visitors placed a higher importance on program content attributes such as live entertainment, dance space and authentic culture, and also have a higher perception of performance for these attributes than first-time visitors. These findings result in direction for management in where to place future resources, as well as implications for promotional and advertising strategies.

This thesis consists of four papers, presented in Chapters 2-5, on the topics large deviations and stochastic simulation, particularly importance sampling. The four papers make theoretical contributions to the development of a new approach for analyzing efficiency of importance sampling algorithms by means of large deviation theory, and to the design of efficient algorithms using the subsolution approach developed by Dupuis and Wang (2007). In the first two papers of the thesis, the random output of an importance sampling algorithm is viewed as a sequence of weighted empirical measures and weighted empirical processes, respectively. The main theoretical results are a Laplace principle for the weighted empirical measures (Paper 1) and a moderate deviation result for the weighted empirical processes (Paper 2). The Laplace principle for weighted empirical measures is used to propose an alternative measure of efficiency based on the associated rate function.The moderate deviation result for weighted empirical processes is an extension of what can be seen as the empirical process version of Sanov's theorem. Together with a delta method for large deviations, established by Gao and Zhao (2011), we show moderate deviation results for importance sampling estimators of the risk measures Value-at-Risk and Expected Shortfall. The final two papers of the thesis are concerned with the design of efficient importance sampling algorithms using subsolutions of partial differential equations of Hamilton-Jacobi type (the subsolution approach). In Paper 3 we show a min-max representation of viscosity solutions of Hamilton-Jacobi equations. In particular, the representation suggests a general approach for constructing subsolutions to equations associated with terminal value problems and exit problems. Since the design of efficient importance sampling algorithms is connected to such subsolutions, the min-max representation facilitates the construction of efficient algorithms. In Paper 4 we consider the problem of constructing efficient importance sampling algorithms for a certain type of Markovian intensity model for credit risk. The min-max representation of Paper 3 is used to construct subsolutions to the associated Hamilton-Jacobi equation and the corresponding importance sampling algorithms are investigated both theoretically and numerically. The thesis begins with an informal discussion of stochastic simulation, followed by brief mathematical introductions to large deviations and importance sampling. / <p>QC 20140424</p>

Background: Uncertainty in parameters is present in many risk assessment and decision making problems and leads to uncertainty in model predictions. Therefore an analysis of the degree of uncertainty around the model inputs is often needed. Importance analysis involves use of quantitative methods aiming at identifying the contribution of uncertain input model parameters to output uncertainty. Expected value of partial perfect information (EVPPI) measure is a current gold- standard technique for measuring parameters importance in health economics models. The current standard approach of estimating EVPPI through performing double Monte Carlo simulation (MCS) can be associated with a long run time. Objective: To investigate different importance analysis techniques with an aim to find alternative technique with shorter run time that will identify parameters with greatest contribution to uncertainty in model output. Methods: A health economics model was updated and served as a tool to implement various importance analysis techniques. Twelve alternative techniques were applied: rank correlation analysis, contribution to variance analysis, mutual information analysis, dominance analysis, regression analysis, analysis of elasticity, ANCOVA, maximum separation distances analysis, sequential bifurcation, double MCS EVPPI,EVPPI-quadrature and EVPPI- single method. Results: Among all these techniques, the dominance measure resulted with the closest correlated calibrated scores when compared with EVPPI calibrated scores. Performing a dominance analysis as a screening method to identify subgroup of parameters as candidates for being most important parameters and subsequently only performing EVPPI analysis on the selected parameters will reduce the overall run time.

Previous research shows that bodily experiences can unconsciously influence perception, judgment, and behavior. However, inconsistency among recent findings in the embodied cognition literature suggests a need for theoretical boundary conditions. While research appears to assume that embodied effects are necessarily implicit (Schnall, 2017), the extant literature has not directly manipulated the role that conscious awareness of bodily states plays in embodied cognition. Dual process theories of social cognition assert that information processing falls along a continuum, from processing that is relatively automatic, effortless, and experiential, to processing that is relatively deliberate, controlled, and rational. Importantly, information processed along the dimensions of this continuum can lead to different outcomes. Thus, if the body influences social cognition in a more implicit manner, experimentally manipulating conscious awareness of a bodily state may lend further insight into when embodiment is attenuated. Six studies tested this possibility in the case of the demonstrated effect of weight sensations on judgments of an abstract idea’s importance (e.g., Ackerman, Nocera, & Bargh, 2010; Jostmann, Lakens, Schubert, 2009). Studies 1 and 2 revealed a curvilinear relationship between increased clipboard weight and ratings of importance such that participants rated a topic as more important when holding a moderately heavy, compared to light, clipboard; however, the importance ratings decreased when the clipboard was very heavy. This curvilinear relationship was not caused by a negative evaluation of the topic or the activation of a different metaphor (burden). In Study 3, ratings of importance increased with a moderately heavy clipboard compared to a light clipboard, but this difference was eliminated by explicitly drawing perceiver's attention to the weight of the clipboard. Study 4 extended the model and showed that even a very heavy clipboard can act as an embodiment of importance when participants are prevented from deliberately processing the weight of the clipboard via a cognitive load manipulation. Study 5 provided limited evidence establishing the role of cognitive motivation in embodiment as measured by need for cognition. However, experimentally manipulating cognitive motivation in Study 6 showed that individuals with higher cognitive motivation were more likely to show the embodied effect when the heft of the clipboard was subtle (i.e., holding a moderately heavy clipboard) whereas those with lower cognitive motivation were more likely to show the embodied effect when the heft of the clipboard was blatant (i.e., holding a very heavy clipboard). Collectively, these studies suggest that embodiment is subject to dual-processes whereby if something in the context draws conscious attention to a stimuli that activates an embodied metaphor, perceivers will no longer use their body as a source of information when processing the stimuli.