9 resultados para Average Run Length (ARL), Bayesian Estimation, Control Chart, g-and-k Distributions, Non-normality

em Digital Commons at Florida International University


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Group testing has long been considered as a safe and sensible relative to one-at-a-time testing in applications where the prevalence rate p is small. In this thesis, we applied Bayes approach to estimate p using Beta-type prior distribution. First, we showed two Bayes estimators of p from prior on p derived from two different loss functions. Second, we presented two more Bayes estimators of p from prior on π according to two loss functions. We also displayed credible and HPD interval for p. In addition, we did intensive numerical studies. All results showed that the Bayes estimator was preferred over the usual maximum likelihood estimator (MLE) for small p. We also presented the optimal β for different p, m, and k.

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Taylor Slough is one of the natural freshwater contributors to Florida Bay through a network of microtidal creeks crossing the Everglades Mangrove Ecotone Region (EMER). The EMER ecological function is critical since it mediates freshwater and nutrient inputs and controls the water quality in Eastern Florida Bay. Furthermore, this region is vulnerable to changing hydrodynamics and nutrient loadings as a result of upstream freshwater management practices proposed by the Comprehensive Everglades Restoration Program (CERP), currently the largest wetland restoration project in the USA. Despite the hydrological importance of Taylor Slough in the water budget of Florida Bay, there are no fine scale (∼1 km2) hydrodynamic models of this system that can be utilized as a tool to evaluate potential changes in water flow, salinity, and water quality. Taylor River is one of the major creeks draining Taylor Slough freshwater into Florida Bay. We performed a water budget analysis for the Taylor River area, based on long-term hydrologic data (1999–2007) and supplemented by hydrodynamic modeling using a MIKE FLOOD (DHI,http://dhigroup.com/) model to evaluate groundwater and overland water discharges. The seasonal hydrologic characteristics are very distinctive (average Taylor River wet vs. dry season outflow was 6 to 1 during 1999–2006) with a pronounced interannual variability of flow. The water budget shows a net dominance of through flow in the tidal mixing zone, while local precipitation and evapotranspiration play only a secondary role, at least in the wet season. During the dry season, the tidal flood reaches the upstream boundary of the study area during approximately 80 days per year on average. The groundwater field measurements indicate a mostly upwards-oriented leakage, which possibly equals the evapotranspiration term. The model results suggest a high importance of groundwater contribution to the water salinity in the EMER. The model performance is satisfactory during the dry season where surface flow in the area is confined to the Taylor River channel. The model also provided guidance on the importance of capturing the overland flow component, which enters the area as sheet flow during the rainy season. Overall, the modeling approach is suitable to reach better understanding of the water budget in the mangrove region. However, more detailed field data is needed to ascertain model predictions by further calibrating overland flow parameters.

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As long as governmental institutions have existed, efforts have been undertaken to reform them. This research examines a particular strategy, coercive controls, exercised through a particular instrument, executive orders, by a singular reformer, the president of the United States. The presidents studied-- Johnson, Nixon, Ford, Carter, Reagan, Bush, and Clinton--are those whose campaigns for office were characterized to varying degrees as against Washington bureaucracy and for executive reform. Executive order issuance is assessed through an examination of key factors for each president including political party affiliation, levels of political capital, and legislative experience. A classification typology is used to identify the topical dimensions and levels of coerciveness. The portrayal of the federal government is analyzed through examination of public, media, and presidential attention. The results show that executive orders are significant management tools for the president. Executive orders also represent an important component of the transition plans for incoming administrations. The findings indicate that overall, while executive orders have not increased in the aggregate, they are more intrusive and significant. When the factors of political party affiliation, political capital, and legislative experience are examined, it reveals a strong relationship between executive orders and previous executive experience, specifically presidents who served as a state governor prior to winning national election as president. Presidents Carter, Reagan, and Clinton (all former governors) have the highest percent of executive orders focusing on the federal bureaucracy. Additionally, the highest percent of forceful orders were issued by former governors (41.0%) as compared to their presidential counterparts who have not served as governors (19.9%). Secondly, political party affiliation is an important, but not significant, predictor for the use of executive orders. Thirdly, management strategies that provide the president with the greatest level of autonomy--executive orders--redefine the concept of presidential power and autonomous action. Interviews of elite government officials and political observers support the idea that executive orders can provide the president with a successful management strategy, requiring less expenditure of political resources, less risk to political capital, and a way of achieving objectives without depending on an unresponsive Congress. ^

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With the rapid globalization and integration of world capital markets, more and more stocks are listed in multiple markets. With multi-listed stocks, the traditional measurement of systematic risk, the domestic beta, is not appropriate since it only contain information from one market. ^ Prakash et al. (1993) developed a technique, the global beta, to capture information from multiple markets wherein the stocks are listed. In this study, the global betas are obtained as well as domestic betas for 704 multi-listed stocks from 59 world equity markets. Welch tests show that domestic betas are not equal across markets, therefore, global beta is more appropriate in a global investment setting. ^ The traditional Capital Asset Pricing Models (CAPM) is also tested with regards to both domestic beta and global beta. The results generally support the positive relationship between stocks returns and global beta while tend to reject this relationship between stocks returns and domestic beta. Further tests of International CAPM with domestic beta and global beta strengthen the conclusion.^

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Using high-resolution measures of aquatic ecosystem metabolism and water quality, we investigated the importance of hydrological inputs of phosphorus (P) on ecosystem dynamics in the oligotrophic, P-limited coastal Everglades. Due to low nutrient status and relatively large inputs of terrestrial organic matter, we hypothesized that the ponds in this region would be strongly net heterotrophic and that pond gross primary production (GPP) and respiration (R) would be the greatest during the “dry,” euhaline estuarine season that coincides with increased P availability. Results indicated that metabolism rates were consistently associated with elevated upstream total phosphorus and salinity concentrations. Pulses in aquatic metabolism rates were coupled to the timing of P supply from groundwater upwelling as well as a potential suite of hydrobiogeochemical interactions. We provide evidence that freshwater discharge has observable impacts on aquatic ecosystem function in the oligotrophic estuaries of the Florida Everglades by controlling the availability of P to the ecosystem. Future water management decisions in South Florida must include the impact of changes in water delivery on downstream estuaries.

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Adequate care of type 2 diabetes is reflected by the individual’s adherence to dietary guidance; yet, few patients are engaged in diabetes self-care at the recommended level, regardless of race/ethnicity. Few studies on the effect of dietary medical advice on diabetes self-management (DSM) and glycemic control have been conducted on Haitian and African American adults with type 2 diabetes. These relationships were assessed in total of 254 Blacks with type 2 diabetes (Haitian Americans = 129; African Americans = 125) recruited from Miami-Dade and Broward Counties, Florida by community outreach methods. Although dietary advice received was not significantly different between the two Black ethnicities, given advice “to follow a diet” as a predictor of “using food groups” was significant for Haitian Americans, but not for African Americans. Haitian Americans who were advised to follow a diet were approximately 3 times more likely to sometimes or often use food groups (or exchange lists) in planning meals. Less than optimal glycemic control (A1C > 7.2) was inversely related to DSM for African Americans; but the relationship was not significant for Haitian Americans. A one unit increase in DSM score decreased the odds ratio point estimate of having less than optimal glycemic control (A1C > 7.2%) by a factor of 0.94 in African Americans. These results suggest that medical advice for diet plans may not be communicated effectively for DSM for some races/ethnicities. Research aimed at uncovering the enablers and barriers of diet management specific to Black ethnicities with type 2 diabetes is recommended.

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As long as governmental institutions have existed, efforts have been undertaken to reform them. This research examines a particular strategy, coercive controls, exercised through a particular instrument, executive orders, by a singular reformer, the president of the United States. The presidents studied- Johnson, Nixon, Ford, Carter, Reagan, Bush, and Clinton-are those whose campaigns for office were characterized to varying degrees as against Washington bureaucracy and for executive reform. Executive order issuance is assessed through an examination of key factors for each president including political party affiliation, levels of political capital, and legislative experience. A classification typology is used to identify the topical dimensions and levels of coerciveness. The portrayal of the federal government is analyzed through examination of public, media, and presidential attention. The results show that executive orders are significant management tools for the president. Executive orders also represent an important component of the transition plans for incoming administrations. The findings indicate that overall, while executive orders have not increased in the aggregate, they are more intrusive and significant. When the factors of political party affiliation, political capital, and legislative experience are examined, it reveals a strong relationship between executive orders and previous executive experience, specifically presidents who served as a state governor prior to winning national election as president. Presidents Carter, Reagan, and Clinton (all former governors) have the highest percent of executive orders focusing on the federal bureaucracy. Additionally, the highest percent of forceful orders were issued by former governors (41.0%) as compared to their presidential counterparts who have not served as governors (19.9%). Secondly, political party affiliation is an important, but not significant, predictor for the use of executive orders. Thirdly, management strategies that provide the president with the greatest level of autonomy-executive orders redefine the concept of presidential power and autonomous action. Interviews of elite government officials and political observers support the idea that executive orders can provide the president with a successful management strategy, requiring less expenditure of political resources, less risk to political capital, and a way of achieving objectives without depending on an unresponsive Congress.

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Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

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Land use and transportation interaction has been a research topic for several decades. There have been efforts to identify impacts of transportation on land use from several different perspectives. One focus has been the role of transportation improvements in encouraging new land developments or relocation of activities due to improved accessibility. The impacts studied have included property values and increased development. Another focus has been on the changes in travel behavior due to better mobility and accessibility. Most studies to date have been conducted in metropolitan level, thus unable to account for interactions spatially and temporally at smaller geographic scales. ^ In this study, a framework for studying the temporal interactions between transportation and land use was proposed and applied to three selected corridor areas in Miami-Dade County, Florida. The framework consists of two parts: one is developing of temporal data and the other is applying time series analysis to this temporal data to identify their dynamic interactions. Temporal GIS databases were constructed and used to compile building permit data and transportation improvement projects. Two types of time series analysis approaches were utilized: univariate models and multivariate models. Time series analysis is designed to describe the dynamic consequences of time series by developing models and forecasting the future of the system based on historical trends. Model estimation results from the selected corridors were then compared. ^ It was found that the time series models predicted residential development better than commercial development. It was also found that results from three study corridors varied in terms of the magnitude of impacts, length of lags, significance of the variables, and the model structure. Long-run effect or cumulated impact of transportation improvement on land developments was also measured with time series techniques. The study offered evidence that congestion negatively impacted development and transportation investments encouraged land development. ^