<p>Chapter 1: Although there are widely accepted theoretical explanations for overexploitation of common-pool resources, empirically we have limited information about the micro-level mechanisms that cause individually efficient exploitation to result in macro inefficiency. This paper conducts the first empirical investigation of common-pool resource users' dynamic and strategic behavior at the micro level. With an application to the North Carolina shrimp fishery, we examine fishermen's strategies in a fully dynamic game that accounts for latent resource dynamics and other players' actions. Combining a simulation-based Conditional Choice Probability estimator and a Pseudo Maximum Likelihood estimator, we recover the profit structure of the fishery from fishermen's repeated choices. Using the estimated structural parameters, we compare the fishermen's actual exploitation path to the socially optimal one under a time-specific limited entry system with transferrable permits, and then quantify the dynamic efficiency costs of common-pool resource use. We find that individual fishermen respond to other users by exerting a higher level of exploitation effort than what is socially optimal. Based on our counterfactual experiments, we estimate the efficiency costs of this behavior to be 17.39\% of the annual revenues from the fishery, which translates into 31.4\% of the rent without deducting the cost of capital. </p><p>Chapter 2: Although hypoxia is a threat to coastal ecosystems, policy makers have limited information about the potential economic impacts on fisheries. Studies using spatially and temporally aggregated data generally fail to detect statistically significant fishery effects of hypoxia. Limited recent work using disaggregated fishing data (microdata) reports modest effects of hypoxia on catches of recreationally harvested species. These prior studies have not accounted for important spatial and temporal aspects of the system, however. For example, the effects of hypoxia on catches may not materialize instantaneously but instead may involve a lagged process with catches reflecting cumulative past exposure to environmental conditions. This paper develops a differenced bioeconomic model to account for the lagged effects of hypoxia on the North Carolina brown shrimp fishery. It integrates high-resolution oxygen monitoring data with fishery-dependent microdata from North Carolina's trip ticket program to investigate the detailed spatial and temporal relationships of hypoxia to commercial fishery harvest. The main finding is that hypoxia potentially resulted in a 12.9\% annual decrease in brown shrimp harvest from 1999-2005. The paper also develops two alternative models---a non-differenced model and a polynomial distributed lag model---and results are consistent with the main model.</p><p>Chapter 3: The emergence of ecosystem-based management suggests that traditional fisheries</p><p>management and protection of environmental quality are increasingly interrelated. Fishery managers, however, have limited control over most sources of marine and estuarine pollution and at best can only adapt to environmental conditions. We develop a bioeconomic model of optimal harvest of an annual species that is subject to an environmental disturbance. We parameterize the model to analyze the effect of hypoxia (low dissolved oxygen) on the optimal harvest path of brown shrimp, a commercially important species that is fished in hypoxic waters in the Gulf of Mexico and in estuaries in the southeastern United States. We find that hypoxia alters the qualitative pattern of optimal harvest and shifts the season opening earlier in the year; more severe hypoxia leads to even earlier season openings. However, the quantitative effects of adapting fishery management to hypoxia in terms of fishery rents are small. This suggests that it is critical for other regulatory agencies to control estuarine pollution, and fishery managers need to generate value from the fishery resources through other means such as rationalization.</p> / Dissertation

<p>This study examines whether trajectories of multiple social role occupancy, measured by level and dynamics of spouse, parent, and worker roles, are associated with mortality and concurrent trajectories of depressive symptoms and self-rated health. I frame hypotheses with role strain, role enhancement, role context, stress process, and life course theories to examine both within-person changes over age and between-person predictors of health status.</p><p></p><p>I use data from the Americans' Changing Lives Study, a nationally representative accelerated cohort panel study of U.S. adults interviewed in 1986, 1989, 1994, and 2001/2 with mortality tracking through 2006. I use latent class analysis to estimate disaggregated trajectories of role occupancy, role strain, role satisfaction, depressive symptoms, and self-rated health across the adult life course. I then use multinomial and logistic regression analyses to examine associations between role trajectories and health outcomes.</p><p>I find that (1) there is significant heterogeneity in trajectories of role occupancy and characteristics across the adult life course; (2) higher levels of social role occupancy are associated with better depressive symptom and mortality outcomes; (3) lower levels of role strain and higher levels of role satisfaction are associated with better depressive symptom outcomes, and (4); the association between role occupancy and health is robust to the inclusion of role characteristics. Thus, I find support for the role enhancement hypothesis in that higher levels of role occupancy are associated with better health outcomes irrespective of reward and strain associated with those roles.</p> / Dissertation

Accompanying with the rapid growth of Internet, people around the world can easily distribute, browse, and share as much information as possible through the Internet. The enormous amount of information, however, causes the information overload problem that is beyond users¡¦ limited information processing ability. Therefore, recommender systems arise to help users to look for useful information when they cannot describe the requirements precisely. The filtering techniques in recommender systems can be divided into content-based filtering (CBF) and collaborative filtering (CF). Although CF is shown to be superior over CBF in literature, personalized document recommendation relies more on CBF simply because of its text content in nature. Nevertheless, document recommendation task provides a good chance to integrate both techniques into a hybrid one, and enhance the overall recommendation performance. The objective of this research is thus to propose a hybrid filtering approach for personalized document recommendation. Particularly, latent Dirichlet allocation to uncover latent semantic structure in documents is incorporated to help us to either obtain robust document similarity in CF, or explore user profiles in CBF. Two experiments are conducted accordingly. The results show that our proposed approach outperforms other counterparts on the recommendation performance, which justifies the feasibility of our proposed approach in real applications.

Developing sound or reliable statistical models for analyzing vehicle crashes is very important in highway safety studies. A difficulty arises when crash data exhibit overdispersion. Over-dispersion caused by unobserved heterogeneity is a serious problem and has been addressed in a variety ways within the negative binomial (NB) modeling framework. However, the true factors that affect heterogeneity are often unknown to researchers, and failure to accommodate such heterogeneity in the model can undermine the validity of the empirical results. Given the limitations of the NB regression model for addressing over-dispersion of crash data due to heterogeneity, this research examined an alternative model formulation that could be used for capturing heterogeneity through the use of finite mixture regression models. A Finite mixture of Poisson or NB regression models is especially useful when the count data were generated from a heterogeneous population. To evaluate these models, Poisson and NB mixture models were estimated using both simulated and empirical crash datasets, and the results were compared to those from a single NB regression model. For model parameter estimation, a Bayesian approach was adopted, since it provides much richer inference than the maximum likelihood approach. Using simulated datasets, it was shown that the single NB model is biased if the underlying cause of heterogeneity is due to the existence of multiple counting processes. The implications could be poor prediction performance and poor interpretation. Using two empirical datasets, the results demonstrated that a two-component finite mixture of NB regression models (FMNB-2) was quite enough to characterize the uncertainty about the crash occurrence, and it provided more opportunities for interpretation of the dataset which are not available from the standard NB model. Based on the models from the empirical dataset (i.e., FMNB-2 and NB models), their relative performances were also examined in terms of hotspot identification and accident modification factors. Finally, using a simulation study, bias properties of the posterior summary statistics for dispersion parameters in FMNB-2 model were characterized, and the guidelines on the choice of priors and the summary statistics to use were presented for different sample sizes and sample-mean values.

The objective of this study is to investigate consumers in the Taiwan region, the situation that when their due rights were being infringed, they had rather accept the unfair treatment from the manufacturers or suppliers, and will not strive for their own rights. In the consumer market, events of consumer right infringement are happening each day, seriously hindering the market order of fair competition. In this moment of the 2007, what are the thoughts within the minds of the consumers in Taiwan ? What are the factors that influence consumers striving for their due rights? In this study, the following issues were reviewed: relationships between manufacturers and consumers; consumer¡¦s cognizance of consumer rights; consumer protection; the roles of the law; government and consumer protection institutions in consumer protection; consumer education; and, consumer self-protection of consumer rights. A survey questionnaire was developed based on five themes of consumers themselves, consumer knowledge, law, government and consumer protection institutions. The survey attempts to understand the internal views of consumers. Consumers in the northern, central and southern Taiwan were randomly sampled according to population distribution. After collecting 170 questionnaires, the responses were coded and analyzed with SAS (statistical software) using Factor Analysis, one-way MANOVA and one-way ANOVA. Several latent factors were extracted, and the difference between consumer gender, age, education background and living region were studied. The results of statistical analysis indicate in 2007, the four main factors affecting consumers¡¦ strive for their rights are: (1) lack of external protection, (2) lack of self-confidence in claiming their rights, (3) dysfunction of consumer protection institutions, and, (4) lack of consumer knowledge. The results further show that the factors differ among living regions, however there is no evidence that there are differences in consumers gender, age and education background. This study has also investigates the level of consumer rights awareness, and the differences in gender, age, education background and living region in such cognizance. The results of statistical analysis show a very low awareness of consumer rights, and there is no evidence to conclude difference between gender, age, education background and living region.

Today, there are user annotated internet sites, user interaction logs, online user communities which are valuable sources of information concerning the personalized recommendation problem. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of the usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented. Based on this flexible probabilistic model, network regularization and model blending approaches are applied on probabilistic latent semantic analysis model as a solution for social trust network usage throughout the collaborative filtering process. The proposed model has outperformed the baseline methods in our experiments. As a result of the research, it is shown that the proposed methods successfully model the rating and social trust data together in a theoretically principled way.

Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user pro

Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.

A latent variable approach was used to (1) examine the relationship between working memory capacity and fluid intelligence, (2) compare the relationship between fluid intelligence and two measures of working memory capacity (complex span and n-back), (3) identify higher-order personality factors and (4) determine the relationship between higher-order personality factors, working memory capacity and fluid intelligence. Confirmatory factor analysis followed by structural equation modeling described the complex span and n-back as highly correlated yet distinct constructs. Consistent with previous research, both measures correlated highly with fluid intelligence. Four higher-order personality factors best modeled the structure of personality. Moreover, these four factors had differential relationship to cognitive constructs. The current research provides a deeper understanding of the relationship between working memory capacity and fluid intelligence, including discrepancies considering the magnitude of the relationship between two types of working memory measures and fluid intelligence, and finally, the influence of a diverse personality structure on working memory capacity and fluid intelligence. Importantly, the study examined these relationships on a broad scale using multiple tasks at a latent level contributing to better understanding of the nature of working memory capacity - fluid intelligence relationship and the influence of personality on higher-order cognition.

Ce travail avait pour objectif de proposer des modèles statistiques pour données longitudinales, hétérogènes et multivariées afin de décrire l'évolution cognitive chez les personnes âgés et son association avec la démence. La cognition étant une quantité non-observée mesurée par des tests psychométriques quantitatifs non Gaussiens, nous avons proposé un modèle nonlinéaire à processus latent dans lequel, l'évolution cognitive représentée par le processus latent est décrite par un modèle linéaire mixte incluant des variables explicatives et un processus Brownien. Les tests psychométriques et le processus latent sont liés par des transformations nonlinéaires estimées prenant en compte des effets différents des variables explicatives. Outre décrire les propriétés métrologiques des tests et faire des recommandations sur le test à utiliser suivant la population étudiée, nous avons pu distinguer l'effet de variables explicatives sur l'évolution cognitive latente de leur effet propre sur les tests. Nous avons ensuite étendu cette approche pour prendre en compte l'hétérogénéité de l'évolution cognitive associée à la survenue d'une démence par deux modèles conjoints à classes latentes. En étudiant d'abord la probabilité de démence en fin de suivi conjointement au déclin cognitif, nous avons mis en évidence deux profils d'évolution représentant le processus de vieillissement normal et le processus de vieillissement pathologique. Puis, en étudiant le risque de démence conjointement au déclin cognitif en fonction de l'âge, nous avons montré 5 formes de déclin cognitifs suivant l'âge associés à des risques différents de démence. Ces deux modèles conjoints nous ont aussi permis de proposer des outils de détection de démence calculés à partir de n'importe quelle information cognitive. Chacun des trois modèles proposés a été évalué par le biais d'études de simulation, puis appliqué sur les données de la cohorte PAQUID et enfin, plusieurs méthodes ont été proposées pour évaluer l'adéquation aux données.