941 resultados para Orthogonal polynomials of a discrete variable


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This thesis consists of four essays on the design and disclosure of compensation contracts. Essays 1, 2 and 3 focus on behavioral aspects of mandatory compensation disclosure rules and of contract negotiations in agency relationships. The three experimental studies develop psychology- based theory and present results that deviate from standard economic predictions. Furthermore, the results of Essay 1 and 2 also have implications for firms’ discretion in how to communicate their top management’s incentives to the capital market. Essay 4 analyzes the role of fairness perceptions for the evaluation of executive compensation. For this purpose, two surveys targeting representative eligible voters as well as investment professionals were conducted. Essay 1 investigates the role of the detailed ‘Compensation Discussion and Analysis’, which is part of the Security and Exchange Commission’s 2006 regulation, on investors’ evaluations of executive performance. Compensation disclosure complying with this regulation clarifies the relationship between realized reported compensation and the underlying performance measures and their target achievement levels. The experimental findings suggest that the salient presentation of executives’ incentives inherent in the ‘Compensation Discussion and Analysis’ makes investors’ performance evaluations less outcome dependent. Therefore, investors’ judgment and investment decisions might be less affected by noisy environmental factors that drive financial performance. The results also suggest that fairness perceptions of compensation contracts are essential for investors’ performance evaluations in that more transparent disclosure increases the perceived fairness of compensation and the performance evaluation of managers who are not responsible for a bad financial performance. These results have important practical implications as firms might choose to communicate their top management’s incentive compensation more transparently in order to benefit from less volatile expectations about their future performance. Similar to the first experiment, the experiment described in Essay 2 addresses the question of more transparent compensation disclosure. However, other than the first experiment, the second experiment does not analyze the effect of a more salient presentation of contract information but the informational effect of contract information itself. For this purpose, the experiment tests two conditions in which the assessment of the compensation contracts’ incentive compatibility, which determines executive effort, is either possible or not. On the one hand, the results suggest that the quality of investors’ expectations about executive effort is improved, but on the other hand investors might over-adjust their prior expectations about executive effort if being confronted with an unexpected financial performance and under-adjust if the financial performance confirms their prior expectations. Therefore, in the experiment, more transparent compensation disclosure does not lead to more correct overall judgments of executive effort and to even lower processing quality of outcome information. These results add to the literature on disclosure which predominantly advocates more transparency. The findings of the experiment however, identify decreased information processing quality as a relevant disclosure cost category. Firms might therefore carefully evaluate the additional costs and benefits of more transparent compensation disclosure. Together with the results from the experiment in Essay 1, the two experiments on compensation disclosure imply that firms should rather focus on their discretion how to present their compensation disclosure to benefit from investors’ improved fairness perceptions and their spill-over on performance evaluation. Essay 3 studies the behavioral effects of contextual factors in recruitment processes that do not affect the employer’s or the applicant’s bargaining power from a standard economic perspective. In particular, the experiment studies two common characteristics of recruitment processes: Pre-contractual competition among job applicants and job applicants’ non-binding effort announcements as they might be made during job interviews. Despite the standard economic irrelevance of these factors, the experiment develops theory regarding the behavioral effects on employees’ subsequent effort provision and the employers’ contract design choices. The experimental findings largely support the predictions. More specifically, the results suggest that firms can benefit from increased effort and, therefore, may generate higher profits. Further, firms may seize a larger share of the employment relationship’s profit by highlighting the competitive aspects of the recruitment process and by requiring applicants to make announcements about their future effort. Finally, Essay 4 studies the role of fairness perceptions for the public evaluation of executive compensation. Although economic criteria for the design of incentive compensation generally do not make restrictive recommendations with regard to the amount of compensation, fairness perceptions might be relevant from the perspective of firms and standard setters. This is because behavioral theory has identified fairness as an important determinant of individuals’ judgment and decisions. However, although fairness concerns about executive compensation are often stated in the popular media and even in the literature, evidence on the meaning of fairness in the context of executive compensation is scarce and ambiguous. In order to inform practitioners and standard setters whether fairness concerns are exclusive to non-professionals or relevant for investment professionals as well, the two surveys presented in Essay 4 aim to find commonalities in the opinions of representative eligible voters and investments professionals. The results suggest that fairness is an important criterion for both groups. Especially, exposure to risk in the form of the variable compensation share is an important criterion shared by both groups. The higher the assumed variable share, the higher is the compensation amount to be perceived as fair. However, to a large extent, opinions on executive compensation depend on personality characteristics, and to some extent, investment professionals’ perceptions deviate systematically from those of non-professionals. The findings imply that firms might benefit from emphasizing the riskiness of their managers’ variable pay components and, therefore, the findings are also in line with those of Essay 1.

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Retinal vein occlusion is a leading cause of visual impairment. Experimental models of this condition based on laser photocoagulation of retinal veins have been described and extensively exploited in mammals and larger rodents such as the rat. However, few reports exist on the use of this paradigm in the mouse. The objective of this study was to investigate a model of branch and central retinal vein occlusion in the mouse and characterize in vivo longitudinal retinal morphology alterations using spectral domain optical coherence tomography. Retinal veins were experimentally occluded using laser photocoagulation after intravenous application of Rose Bengal, a photo-activator dye enhancing thrombus formation. Depending on the number of veins occluded, variable amounts of capillary dropout were seen on fluorescein angiography. Vascular endothelial growth factor levels were markedly elevated early and peaked at day one. Retinal thickness measurements with spectral domain optical coherence tomography showed significant swelling (p<0.001) compared to baseline, followed by gradual thinning plateauing two weeks after the experimental intervention (p<0.001). Histological findings at day seven correlated with spectral domain optical coherence tomography imaging. The inner layers were predominantly affected by degeneration with the outer nuclear layer and the photoreceptor outer segments largely preserved. The application of this retinal vein occlusion model in the mouse carries several advantages over its use in other larger species, such as access to a vast range of genetically modified animals. Retinal changes after experimental retinal vein occlusion in this mouse model can be non-invasively quantified by spectral domain optical coherence tomography, and may be used to monitor effects of potential therapeutic interventions.

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The time variable Earth’s gravity field contains information about the mass transport within the system Earth, i.e., the relationship between mass variations in the atmosphere, oceans, land hydrology, and ice sheets. For many years, satellite laser ranging (SLR) observations to geodetic satellites have provided valuable information of the low-degree coefficients of the Earth’s gravity field. Today, the Gravity Recovery and Climate Experiment (GRACE) mission is the major source of information for the time variable field of a high spatial resolution. We recover the low-degree coefficients of the time variable Earth’s gravity field using SLR observations up to nine geodetic satellites: LAGEOS-1, LAGEOS-2, Starlette, Stella, AJISAI, LARES, Larets, BLITS, and Beacon-C. We estimate monthly gravity field coefficients up to degree and order 10/10 for the time span 2003–2013 and we compare the results with the GRACE-derived gravity field coefficients. We show that not only degree-2 gravity field coefficients can be well determined from SLR, but also other coefficients up to degree 10 using the combination of short 1-day arcs for low orbiting satellites and 10-day arcs for LAGEOS-1/2. In this way, LAGEOS-1/2 allow recovering zonal terms, which are associated with long-term satellite orbit perturbations, whereas the tesseral and sectorial terms benefit most from low orbiting satellites, whose orbit modeling deficiencies are minimized due to short 1-day arcs. The amplitudes of the annual signal in the low-degree gravity field coefficients derived from SLR agree with GRACE K-band results at a level of 77 %. This implies that SLR has a great potential to fill the gap between the current GRACE and the future GRACE Follow-On mission for recovering of the seasonal variations and secular trends of the longest wavelengths in gravity field, which are associated with the large-scale mass transport in the system Earth.

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BACKGROUND After cardiac surgery with cardiopulmonary bypass (CPB), acquired coagulopathy often leads to post-CPB bleeding. Though multifactorial in origin, this coagulopathy is often aggravated by deficient fibrinogen levels. OBJECTIVE To assess whether laboratory and thrombelastometric testing on CPB can predict plasma fibrinogen immediately after CPB weaning. PATIENTS / METHODS This prospective study in 110 patients undergoing major cardiovascular surgery at risk of post-CPB bleeding compares fibrinogen level (Clauss method) and function (fibrin-specific thrombelastometry) in order to study the predictability of their course early after termination of CPB. Linear regression analysis and receiver operating characteristics were used to determine correlations and predictive accuracy. RESULTS Quantitative estimation of post-CPB Clauss fibrinogen from on-CPB fibrinogen was feasible with small bias (+0.19 g/l), but with poor precision and a percentage of error >30%. A clinically useful alternative approach was developed by using on-CPB A10 to predict a Clauss fibrinogen range of interest instead of a discrete level. An on-CPB A10 ≤10 mm identified patients with a post-CPB Clauss fibrinogen of ≤1.5 g/l with a sensitivity of 0.99 and a positive predictive value of 0.60; it also identified those without a post-CPB Clauss fibrinogen <2.0 g/l with a specificity of 0.83. CONCLUSIONS When measured on CPB prior to weaning, a FIBTEM A10 ≤10 mm is an early alert for post-CPB fibrinogen levels below or within the substitution range (1.5-2.0 g/l) recommended in case of post-CPB coagulopathic bleeding. This helps to minimize the delay to data-based hemostatic management after weaning from CPB.

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We have previously isolated anti-FcepsilonRIalpha autoantibodies from phage libraries of healthy donors and urticaria patients. Strikingly, the same antibody, LTMalpha15, was isolated from both libraries. Sequence analysis revealed a germline configuration of the LTMalpha15 variable heavy (V(H)) chain with a slightly mutated variable light (V(L)) chain supporting its classification as a natural autoantibody. Distribution analysis of anti-FcepsilonRIalpha autoantibodies by functional or serological tests delivered conflicting data. For this reason we have developed a new real-time PCR to analyse the distribution of LTMalpha15V(H) in healthy donors and urticaria patients. Our new bioinformatic program permitted the design of a minor groove binder (MGB) TaqMan probe that specifically detected the LTMalpha15V(H). We were able to demonstrate a broad range of rearranged V(H) gene copy number without any correlation to the state of health. Monitoring LTMalpha15V(H) gene copy number in a single donor over a period of 70 days revealed a time-related fluctuation of circulating B cells carrying LTMalpha15V(H). We propose that our real-time PCR may serve as a model for the quantification of natural antibody sequences at a monoclonal level.

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In this study the relationship of religiosity and value priorities is differentiated, based on a multidimensional measurement of different contents of religiosity. The structure of values is conceptualized using Schwartz’ (1992) two orthogonal dimensions of Self-transcendence vs. Self-enhancement and Openness to change vs. Conservation. The relations between these two dimensions and eight religious contents, ranging from open-minded to more close-minded forms of religiosity, were tested in a sample of church attenders (N = 685), gathered in Germany. The results show, that depending on the content of religiosity, different values are preferred (self-direction, universalism, benevolence, tradition and security values). The results indicate the importance of the content of religiosity for predicting value-loaded behaviors.

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Soil microbial biomass is a key determinant of carbon dynamics in the soil. Several studies have shown that soil microbial biomass significantly increases with plant species diversity, but it remains unclear whether plant species diversity can also stabilize soil microbial biomass in a changing environment. This question is particularly relevant as many global environmental change (GEC) factors, such as drought and nutrient enrichment, have been shown to reduce soil microbial biomass. Experiments with orthogonal manipulations of plant diversity and GEC factors can provide insights whether plant diversity can attenuate such detrimental effects on soil microbial biomass. Here, we present the analysis of 12 different studies with 14 unique orthogonal plant diversity × GEC manipulations in grasslands, where plant diversity and at least one GEC factor (elevated CO2, nutrient enrichment, drought, earthworm presence, or warming) were manipulated. Our results show that higher plant diversity significantly enhances soil microbial biomass with the strongest effects in long-term field experiments. In contrast, GEC factors had inconsistent effects with only drought having a significant negative effect. Importantly, we report consistent non-significant effects for all 14 interactions between plant diversity and GEC factors, which indicates a limited potential of plant diversity to attenuate the effects of GEC factors on soil microbial biomass. We highlight that plant diversity is a major determinant of soil microbial biomass in experimental grasslands that can influence soil carbon dynamics irrespective of GEC.

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Steiner’s tube formula states that the volume of an ϵ-neighborhood of a smooth regular domain in Rn is a polynomial of degree n in the variable ϵ whose coefficients are curvature integrals (also called quermassintegrals). We prove a similar result in the sub-Riemannian setting of the first Heisenberg group. In contrast to the Euclidean setting, we find that the volume of an ϵ-neighborhood with respect to the Heisenberg metric is an analytic function of ϵ that is generally not a polynomial. The coefficients of the series expansion can be explicitly written in terms of integrals of iteratively defined canonical polynomials of just five curvature terms.

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T-cell lymphomas from AKR mice were studied to determine their potential as a model of T-cell differentiation. Homogeneous tumor cell lines have been used as model to study normal lymphocyte subpopulations, including differentiation lineages, functional properties, and the inducibility to maturation. The underlying concept is that each lymphoid tumor represents a monoclonal neoplastic proliferation of a discrete lymphoid subpopulation arrested at a particular differentiation stage.^ Individual tumors were analyzed to determine the extent of intertumor heterogeneity, and to determine whether lymphomas represented different thymocyte subsets, by determining the cell-surface antigenic phenotype, PNA-binding capacity, and terminal deoxynucleotidyl transferase (TdT) activity. Splenic and thymic tumor cells were compared to determine if the particular lymphoid microenvironment influenced T-cell marker expression. Several of the lymphomas were passaged in syngeneic hosts to verify the original tumor phenotype and to assess the stability of the cell surface and TdT phenotype after transplantation.^ Lymphomas were adapted to in vitro culture to determine whether the T-cell phenotype was maintained in the absence of the host microenvironment. Clonal progeny were analyzed and compared with each other and with parent cell lines to determine the extent of intratumor heterogeneity in this lymphoma system. Parent and cloned cell lines were passaged in vivo to determine whether alterations in surface phenotype occurred after transplantation.^ Our investigation has verified that most spontaneous AKR lymphomas phenotypically resemble known T-cell subsets, including both immature and mature thymic subpopulations. The in vitro lines, however, expressed a highly unstable phenotype in culture that included loss of Ly-1 and Ly-2 antigen expression. After transplantation in vivo, the in vitro lines exhibited alterations in phenotype, including re-expression of Ly antigen on some lymphomas. The inducibility of T-cell antigen markers on tumor cell lines passaged in vivo suggests that the in vitro lines may serve as a possible model system to study the molecular events involved in gene expression in the T-cell system. ^

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Public preferences for policy are formed in a little-understood process that is not adequately described by traditional economic theory of choice. In this paper I suggest that U.S. aggregate support for health reform can be modeled as tradeoffs among a small number of behavioral values and the stage of policy development. The theory underlying the model is based on Samuelson, et al.'s (1986) work and Wilke's (1991) elaboration of it as the Greed/Efficiency/Fairness (GEF) hypothesis of motivation in the management of resource dilemmas, and behavioral economics informed by Kahneman and Thaler's prospect theory. ^ The model developed in this paper employs ordered probit econometric techniques applied to data derived from U.S. polls taken from 1990 to mid-2003 that measured support for health reform proposals. Outcome data are four-tiered Likert counts; independent variables are dummies representing the presence or absence of operationalizations of each behavioral variable, along with an integer representing policy process stage. Marginal effects of each independent variable predict how support levels change on triggering that variable. Model estimation results indicate a vanishingly small likelihood that all coefficients are zero and all variables have signs expected from model theory. ^ Three hypotheses were tested: support will drain from health reform policy as it becomes increasingly well-articulated and approaches enactment; reforms appealing to fairness through universal health coverage will enjoy a higher degree of support than those targeted more narrowly; health reforms calling for government operation of the health finance system will achieve lower support than those that do not. Model results support the first and last hypotheses. Contrary to expectations, universal health care proposals did not provide incremental support beyond those targeted to “deserving” populations—children, elderly, working families. In addition, loss of autonomy (e.g. restrictions on choice of care giver) is found to be the “third rail” of health reform with significantly-reduced support. When applied to a hypothetical health reform in which an employer-mandated Medical Savings Account policy is the centerpiece, the model predicts support that may be insufficient to enactment. These results indicate that the method developed in the paper may prove valuable to health policy designers. ^

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Gastrointestinal stromal tumors (GIST) represent 80% of sarcoma arising from the GI tract. The inciting event in tumor progression is mutation of the kit or, rarely, platelet derived growth factor receptor-α (PDGFR) gene. These mutations encode ligand independent, constitutively active proteins: Kit or PDGFR. ^ These tumors are notoriously chemo and radio resistant. Historically, patients with advanced disease realized a median overall survival of 9 months. However, with modern management of GIST with imatinib mesylate (Novartis), a small molecule inhibitor of the Kit, PDGFR, and Abl tyrosine kinases, patients now realize a median overall survival greater than 30 months. However, almost half of patients present with surgically resectable GIST and the utility of imatinib in this context has not been prospectively studied. Also, therapeutic benefit of imatinib is variable from patient to patient and alternative targeted therapy is emerging as potential alternatives to imatinib. Thus, elucidating prognostic factors for patients with GIST in the imatinib-era is crucial to providing optimal care to each particular patient. Moreover, the exact mechanism of action of imatinib in GIST is not fully understood. Therefore, physicians find difficulty in accurately predicting which patient will benefit from imatinib, how to assess response to therapy, and the time at which to assess response. ^ I have hypothesized that imatinib is tolerable and clinically beneficial in the context of surgery, VEGF expression and kit non-exon 11 genotypes portend poor survival on imatinib therapy, and imatinib's mechanism of action is in part due to anti-vascular effects and inhibition of the Kit/SCF signaling axis of tumor-associated endothelial cells. ^ Results herein demonstrate that imatinib is safe and increases the duration of disease-free survival when combined with surgery. Radiographic and molecular (namely, apoptosis) changes occur within 3 days of imatinib initiation. I illustrate that non-exon 11 mutant genotypes and VEGF are poor prognostic factors for patients treated with imatinib. These findings may allow for patient stratification to emerging therapies rather than imatinib. I show that imatinib has anti-vascular effects via inducing tumor endothelial cell apoptosis perhaps by abrogation of the Kit/SCF signaling axis. ^

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Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^

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Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

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Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

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The Greenland Ice Sheet Project 2 (GISP2) core can enhance our understanding of the relationship between parameters measured in the ice in central Greenland and variability in the ocean, atmosphere, and cryosphere of the North Atlantic Ocean and adjacent land masses. Seasonal (summer, winter) to annual responses of dD and deuterium excess isotopic signals in the GISP2 core to the seesaw in winter temperatures between West Greenland and northern Europe from A.D. 1840 to 1970 are investigated. This seesaw represents extreme modes of the North Atlantic Oscillation, which also influences sea surface temperatures (SSTs), atmospheric pressures, geostrophic wind strength, and sea ice extents beyond the winter season. Temperature excursions inferred from the dD record during seesaw/extreme NAO mode years move in the same direction as the West Greenland side of the seesaw. Symmetry with the West Greenland side of the seesaw suggests a possible mechanism for damping in the ice core record of the lowest decadal temperatures experienced in Europe from A.D. 1500 to 1700. Seasonal and annual deuterium excess excursions during seesaw years show negative correlation with dD. This suggests an isotopic response to a SST/ land temperature seesaw. The isotopic record from GISP2 may therefore give information on both ice sheet and sea surface temperature variability. Cross-plots of dD and d show a tendency for data to be grouped according to the prevailing mode of the seesaw, but do not provide unambiguous identification of individual seesaw years. A combination of ice core and tree ring data sets may allow more confident identification of GA and GB (extreme NAO mode) years prior to 1840.