Thermal Emission from Transiting Very-Hot Jupiters: Prospects for Ground-based Detection at Optical Wavelengths

Very hot Jupiters (VHJs) are defined as Jupiter-mass extrasolar planets with orbital periods shorter than three days. For low albedos the effective temperatures of irradiated VHJs can reach 2500-3000 K. Thermal emission from VHJs is therefore potentially strong at optical wavelengths. We explore the prospects of detecting optical-wavelength thermal emission during secondary eclipse with existing ground-based telescopes. We show that OGLE-TR-56b and OGLE-TR-132b are the best suited candidates for detection, and that the prospects are highest around z'-band (~0.9 microns). We also speculate that any newly discovered VHJs with the right combination of orbital separation and host star parameters could be thermally detected in the optical. The lack of detections would still provide constraints on the planetary albedos and re-radiation factors.Comment: accepted for publication on ApJ

Health Care Fraud

Provides an overview of trends in fraud and abuse involving private insurance, Medicaid, and Medicare; types of schemes; risk factors; and consequences. Examines federal and state laws aimed at healthcare fraud, reported cases, and enforcement efforts

A HALF-GRAPH DEPTH FOR FUNCTIONAL DATA

A recent and highly attractive area of research in statistics is the analysis of functional data. In this paper a new definition of depth for functional observations is introduced based on the notion of “half-graph” of a curve. It has computational advantages with respect to other concepts of depth previously proposed. The half-graph depth provides a natural criterion to measure the centrality of a function within a sample of curves. Based on this depth a sample of curves can be ordered from the center outward and L-statistics are defined. The properties of the half-graph depth, such as the consistency and uniform convergence, are established. A simulation study shows the robustness of this new definition of depth when the curves are contaminated. Finally real data examples are analyzed.

DEPTH-BASED CLASSIFICATION FOR FUNCTIONAL DATA

Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods.

ON THE CONCEPT OF DEPTH FOR FUNCTIONAL DATA

The statistical analysis of functional data is a growing need in many research areas. We propose a new depth notion for functional observations based on the graphic representation of the curves. Given a collection of functions, it allows to establish the centrality of a function and provides a natural center-outward ordering of the sample curves. Robust statistics such as the median function or a trimmed mean function can be defined from this depth definition. Its finite-dimensional version provides a new depth for multivariate data that is computationally very fast and turns out to be convenient to study high-dimensional observations. The natural properties are established for the new depth and the uniform consistency of the sample depth is proved. Simulation results show that the trimmed mean presents a better behavior than the mean for contaminated models. Several real data sets are considered to illustrate this new concept of depth. Finally, we use this new depth to generalize to functions the Wilcoxon rank sum test. It allows to decide whether two groups of curves come from the same population. This functional rank test is applied to girls and boys growth curves concluding that they present different growth patterns.

DEPTH-BASED INFERENCE FOR FUNCTIONAL DATA

We propose robust inference tools for functional data based on the notion of depth for curves. We extend the ideas of trimmed regions, contours and central regions to functions and study their structural properties and asymptotic behavior. Next, we introduce a scale curve to describe dispersion in a sample of functions. The computational burden of these techniques is not heavy and so they are also adequate to analyze high-dimensional data. All these inferential methods are applied to different real data sets.

Data Analytics Strategies for Growth and Sustainability

Implementing data analytics impacts organizations financially and operationally. Business leaders can improve operational capacity and increase profitability and sustainability when data analytics are embedded and used throughout an organization. Grounded in the Baldrige excellence framework, the purpose of this qualitative single case study was to explore strategies senior leaders used to successfully implement data analytics to improve performance management and long-term sustainability. Data were collected using semistructured interviews and a review of organizational documents, including a review and assessment of the company website. The themes that emerged from the thematic data analysis were (a) process strengths, (b) process opportunities, (c) results strengths, and (d) results opportunities. A key recommendation is for leaders to integrate multi-tiered strategic planning to ensure alignment with overall company objectives. The implications for positive social change include the potential for creating new job opportunities and promoting a collaborative working environment for employees. When businesses thrive, their employees may gain improved work compensation

Mitigating the Effects of Churning Under the Affordable Care Act: Lessons from Medicaid

Through a combination of three needs-based public programs— Medicaid, the Children's Health Insurance Program, and tax credits for purchasing private plans in the new marketplaces—the Affordable Care Act can potentially ensure continuous coverage for many low- and moderate-income Americans. At the same time, half of individuals with incomes at less than twice the poverty level will experience a form of "churning" in their coverage; as changes occur in their life or work circumstances, they will need to switch among these three coverage sources. For many, churning will entail not only changes in covered benefits and cost-sharing, but also in care, owing to differences in provider networks. Strategies for mitigating churning's effects are complex and require time to implement. For the short term, however, the experiences of 17 states with policies aimed at smoothing transitions between health plans offer lessons for ensuring care continuit