927 resultados para marginal likelihood


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The past few decades have brought many changes to the dental practice and the technology has become ready available. The result of a satisfactory rehabilitation treatment basically depends on the balance between biological and mechanical factors. The marginal adaptation of crowns and prosthetic structures is vital factor for long-term success. The development of CAD / CAM technology in the manufacture of dental prostheses revolutionized dentistry, this technology is capable of generating a virtual model from the direct digital scanning from the mouth, casts or impressions. It allows the planning and design of the structure in a computered software. The virtual projects are obtained with high precision and a significant reduction in clinical and laboratory time. Thus, the present study (Chapters 1, 2 and 3) computed microtomography was used to evaluate, different materials, different CAD/CAM systems, different ways of obtaining virtual model (with direct or indirect scanning), and in addition, also aims to evaluate the influence of cementing agent in the final adaptation of crowns and copings obtained by CAD / CAM. Furthermore, this study (Chapter 4, 5 and 6) also aims to evaluate significant differences in vertical and horizontal misfits in abutment-free frameworks on external hexagon implants (HE) using full castable UCLAs, castable UCLAs with cobalt-chromium pre-machined bases and obtained by CAD / CAM with CoCr or Zirconia by different scanning and milling systems. For this, the scanning electron microscopy and interferometry were used. It was concluded that the CAD / CAM technology is capable to produce restorations, copings and screw-retained implant-supported frameworks in different materials and systems offering satisfactory results of marginal accuracy, with significative reduction in clinical and laboratory time.

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Acknowledgements This research has been conducted using the UK Biobank resource, and was funded by the University of Aberdeen. The authors have no conflicts of interest to declare.

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My dissertation has three chapters which develop and apply microeconometric tech- niques to empirically relevant problems. All the chapters examines the robustness issues (e.g., measurement error and model misspecification) in the econometric anal- ysis. The first chapter studies the identifying power of an instrumental variable in the nonparametric heterogeneous treatment effect framework when a binary treat- ment variable is mismeasured and endogenous. I characterize the sharp identified set for the local average treatment effect under the following two assumptions: (1) the exclusion restriction of an instrument and (2) deterministic monotonicity of the true treatment variable in the instrument. The identification strategy allows for general measurement error. Notably, (i) the measurement error is nonclassical, (ii) it can be endogenous, and (iii) no assumptions are imposed on the marginal distribution of the measurement error, so that I do not need to assume the accuracy of the measure- ment. Based on the partial identification result, I provide a consistent confidence interval for the local average treatment effect with uniformly valid size control. I also show that the identification strategy can incorporate repeated measurements to narrow the identified set, even if the repeated measurements themselves are endoge- nous. Using the the National Longitudinal Study of the High School Class of 1972, I demonstrate that my new methodology can produce nontrivial bounds for the return to college attendance when attendance is mismeasured and endogenous.

The second chapter, which is a part of a coauthored project with Federico Bugni, considers the problem of inference in dynamic discrete choice problems when the structural model is locally misspecified. We consider two popular classes of estimators for dynamic discrete choice models: K-step maximum likelihood estimators (K-ML) and K-step minimum distance estimators (K-MD), where K denotes the number of policy iterations employed in the estimation problem. These estimator classes include popular estimators such as Rust (1987)’s nested fixed point estimator, Hotz and Miller (1993)’s conditional choice probability estimator, Aguirregabiria and Mira (2002)’s nested algorithm estimator, and Pesendorfer and Schmidt-Dengler (2008)’s least squares estimator. We derive and compare the asymptotic distributions of K- ML and K-MD estimators when the model is arbitrarily locally misspecified and we obtain three main results. In the absence of misspecification, Aguirregabiria and Mira (2002) show that all K-ML estimators are asymptotically equivalent regardless of the choice of K. Our first result shows that this finding extends to a locally misspecified model, regardless of the degree of local misspecification. As a second result, we show that an analogous result holds for all K-MD estimators, i.e., all K- MD estimator are asymptotically equivalent regardless of the choice of K. Our third and final result is to compare K-MD and K-ML estimators in terms of asymptotic mean squared error. Under local misspecification, the optimally weighted K-MD estimator depends on the unknown asymptotic bias and is no longer feasible. In turn, feasible K-MD estimators could have an asymptotic mean squared error that is higher or lower than that of the K-ML estimators. To demonstrate the relevance of our asymptotic analysis, we illustrate our findings using in a simulation exercise based on a misspecified version of Rust (1987) bus engine problem.

The last chapter investigates the causal effect of the Omnibus Budget Reconcil- iation Act of 1993, which caused the biggest change to the EITC in its history, on unemployment and labor force participation among single mothers. Unemployment and labor force participation are difficult to define for a few reasons, for example, be- cause of marginally attached workers. Instead of searching for the unique definition for each of these two concepts, this chapter bounds unemployment and labor force participation by observable variables and, as a result, considers various competing definitions of these two concepts simultaneously. This bounding strategy leads to partial identification of the treatment effect. The inference results depend on the construction of the bounds, but they imply positive effect on labor force participa- tion and negligible effect on unemployment. The results imply that the difference- in-difference result based on the BLS definition of unemployment can be misleading

due to misclassification of unemployment.

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OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

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If a church reflects its larger community, it will have more dynamic interactions among different people. Current U.S. communities consist of very diverse people who have different socioeconomic and cultural backgrounds. Since the mid 20th century, various immigrant communities who have dissimilar cultural, religious, and linguistic traditions have accelerated the need of change in American churches. The drastic cultural change has demanded churches to equip their lay and clergy leaders with multicultural competencies for effective ministries.

My thesis explores imaginative leadership in cultural crossroads. Emphasizing the leadership imagination of cross-cultural ministry, I approach it in biblical, theological, and missional perspectives. In this dynamic cultural milieu, the study topic may help the church renew its ecclesial purpose by seeing cross-cultural ministry as a creative opportunity to reach out to more diverse people of God. I begin with a conceptual framework for cross-cultural ministry and cultural intelligence. Then I explain why cross-cultural ministry is significant and how it enhances the spirit of Christ Jesus. As I develop the thesis, I discuss leadership challenge and development in the cross-cultural ministry context. This thesis may contribute to equipping lay and clergy leaders by overcoming the homogeneous ‘in-group’ mindset in the church.

The primary focus is on developing marginal leadership of church in the post-Christendom era. Church leaders must creatively hold the tension between the current church context and Christian faith resources and seek a hopeful resolution as a third way through integrative thought process. While conventional leadership emphasizes a better choice out of the given options, marginal leadership takes time for integrative thought process to seek a new direction for the future. Conventional leaders take the center with their power, status, and prestige, but marginal leaders position themselves on the edge. Leading from the edge is a distinctive cross-cultural leadership and is based on the servant leadership of Jesus Christ who put himself as a servant for the marginalized. By serving and relating to others on the margin, this imaginative leadership may make appropriate changes desired in today’s American churches.

In addition to academic research, I looked into the realities of cross-cultural leadership in the local churches through congregational studies. I speculated that church leadership involves both laity and clergy and that it can be enhanced. All Christians are called to serve the Lord according to their gifts, and it is crucial for lay and clergy persons to develop their leadership character and skills. In particular, as humans are contextualized with their own cultures, church leaders often confront great challenges in cross-cultural or multicultural situations. Through critical thoughts and imaginative leadership strategies, however, they can overcome intrinsic human prejudice and obstacles.

Through the thesis project, I have reached four significant conclusions. First, cultural intelligence is an essential leadership capacity for all church leaders. As the church consists of more diverse cultural people today, its leaders need to have cultural competencies. In particular, cross-cultural leaders must be equipped with cultural intelligence. Cross-cultural ministry is not a simple byproduct of social change, but a creative strategy to open a door to bring God’s reconciliation among diverse people. Accordingly, church leaders are to be well prepared to effectively cope with the challenges of cultural interactions. Second, both lay and clergy leaders’ imaginative leadership is crucial for leading the congregation. While conventional leadership puts an emphasis on selecting a better choice based on the principle of opportunity cost, imaginative leaders critically consider the present church situations and Christian faith values together in integrative thoughts and pursue a third way as the congregation’s future hope. Third, cross-cultural leadership has a unique characteristic of leading from the edge and promotes God’s justice and peaceable relationships among different people. By leading the congregation from the edge, church leaders may experience the heart of Christ Jesus who became the friend of the marginalized. Fourth, the ‘homogeneous unit principle’ theory has its limit for today’s complex ‘inter-group’ community context. The church must be a welcoming and embracing faith community for all people. Cross-cultural ministry may become an entrance door for a more peaceable and reconciling life among different people. By building solidarity with others, the church may experience a kingdom reality.

This thesis focuses on the mission of the church and marginal leadership of church leaders in ever-changing cultural crossroads. The church becomes a hope in the broken and apathetic world, and Christians are called to build relationships inside and beyond the church. It is significant for church leaders to be faithfully present on the margin and relate to diverse people. By consistently positioning themselves on the margin, they can build relationships with new and diverse people and shape a faithful life pattern for others.

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Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this paper, we extend mixtures of g-priors to GLMs by assigning the truncated Compound Confluent Hypergeometric (tCCH) distribution to 1/(1+g) and illustrate how this prior distribution encompasses several special cases of mixtures of g-priors in the literature, such as the Hyper-g, truncated Gamma, Beta-prime, and the Robust prior. Under an integrated Laplace approximation to the likelihood, the posterior distribution of 1/(1+g) is in turn a tCCH distribution, and approximate marginal likelihoods are thus available analytically. We discuss the local geometric properties of the g-prior in GLMs and show that specific choices of the hyper-parameters satisfy the various desiderata for model selection proposed by Bayarri et al, such as asymptotic model selection consistency, information consistency, intrinsic consistency, and measurement invariance. We also illustrate inference using these priors and contrast them to others in the literature via simulation and real examples.

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The purpose of this study was to assess the effect of performance feedback on Athletic Trainers’ (ATs) perceived knowledge (PK) and likelihood to pursue continuing education (CE). The investigation was grounded in the theories of “the definition of the situation” (Thomas & Thomas, 1928) and the “illusion of knowing,” (Glenberg, Wilkinson, & Epstein, 1982) suggesting that PK drives behavior. This investigation measured the degree to which knowledge gap predicted CE seeking behavior by providing performance feedback designed to change PK. A pre-test post-test control-group design was used to measure PK and likelihood to pursue CE before and after assessing actual knowledge. ATs (n=103) were randomly sampled and assigned to two groups, with and without performance feedback. Two independent samples t-tests were used to compare groups on the difference scores of the dependent variables. Likelihood to pursue CE was predicted by three variables using multiple linear regression: perceived knowledge, pre-test likelihood to pursue CE, and knowledge gap. There was a 68.4% significant difference (t101= 2.72, p=0.01, ES=0.45) between groups in the change scores for likelihood to pursue CE because of the performance feedback (Experimental group=13.7% increase; Control group= 4.3% increase). The strongest relationship among the dependent variables was between pre-test and post-test measures of likelihood to pursue CE (F2,102=56.80, p<0.01, r=0.73, R2=0.53). The pre- and post-test predictive relationship was enhanced when group was included in the model. In this model [YCEpost=0.76XCEpre-0.34 Xgroup+2.24+E], group accounted for a significant amount of unique variance in predicting CE while the pre-test likelihood to pursue CE variable was held constant (F3,102=40.28, p<0.01,: r=0.74, R2=0.55). Pre-test knowledge gap, regardless of group allocation, was a linear predictor of the likelihood to pursue CE (F1,102=10.90, p=.01, r=.31, R2=.10). In this investigation, performance feedback significantly increased participants’ likelihood to pursue CE. Pre-test knowledge gap was a significant predictor of likelihood to pursue CE, regardless if performance feedback was provided. ATs may have self-assessed and engaged in internal feedback as a result of their test-taking experience. These findings indicate that feedback, both internal and external, may be necessary to trigger CE seeking behavior.

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Records of the spatial and temporal variability of Arctic Ocean sea ice are of significance for understanding the causes of the dramatic decrease in Arctic sea-ice cover of recent years. In this context, the newly developed sea-ice proxy IP25, a mono-unsaturated highly branched isoprenoid alkene with 25 carbon atoms biosynthesized specifically by sea-ice associated diatoms and only found in Arctic and sub-Arctic marine sediments, has been used to reconstruct the recent spatial sea-ice distribution. The phytoplankton biomarkers 24S-brassicasterol and dinosterol were determined alongside IP25 to distinguish ice-free or permanent ice conditions, and to estimate the sea-ice conditions semi-quantitatively by means of the phytoplankton-IP25 index (PIP25). Within our study, for the first time a comprehensive data set of these biomarkers was produced using fresh and deep-frozen surface sediment samples from the Central Arctic Ocean proper (>80°N latitude) characterised by a permanent ice cover today and recently obtained surface sediment samples from the Chukchi Plateau/Basin partly covered by perennial sea ice. In addition, published and new data from other Arctic and sub-Arctic regions were added to generate overview distribution maps of IP25 and phytoplankton biomarkers across major parts of the modern Arctic Ocean. These comprehensive biomarker data indicate perennial sea-ice cover in the Central Arctic, ice-free conditions in the Barents Sea and variable sea-ice situations in other marginal seas. The low but more than zero values of biomarkers in the Central Arctic supported the low in-situ productivity there. The PIP25 index values reflect modern sea-ice conditions better than IP25 alone and show a positive correlation with spring/summer sea ice. When calculating and interpreting PIP25 index as a (semi-quantitative) proxy for reconstructions of present and past Arctic sea-ice conditions from different Arctic/sub-Arctic areas, information of the source of phytoplankton biomarkers and the possible presence of allochthonous biomarkers is needed, and the records of the individual biomarkers always should be considered as well.

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When we study the variables that a ffect survival time, we usually estimate their eff ects by the Cox regression model. In biomedical research, e ffects of the covariates are often modi ed by a biomarker variable. This leads to covariates-biomarker interactions. Here biomarker is an objective measurement of the patient characteristics at baseline. Liu et al. (2015) has built up a local partial likelihood bootstrap model to estimate and test this interaction e ffect of covariates and biomarker, but the R code developed by Liu et al. (2015) can only handle one variable and one interaction term and can not t the model with adjustment to nuisance variables. In this project, we expand the model to allow adjustment to nuisance variables, expand the R code to take any chosen interaction terms, and we set up many parameters for users to customize their research. We also build up an R package called "lplb" to integrate the complex computations into a simple interface. We conduct numerical simulation to show that the new method has excellent fi nite sample properties under both the null and alternative hypothesis. We also applied the method to analyze data from a prostate cancer clinical trial with acid phosphatase (AP) biomarker.