A comparison of in-sample forecasting methods

In-sample forecasting is a recent continuous modification of well-known forecasting methods based on aggregated data. These aggregated methods are known as age-cohort methods in demography, economics, epidemiology and sociology and as chain ladder in non-life insurance. Data is organized in a two-way table with age and cohort as indices, but without measures of exposure. It has recently been established that such structured forecasting methods based on aggregated data can be interpreted as structured histogram estimators. Continuous in-sample forecasting transfers these classical forecasting models into a modern statistical world including smoothing methodology that is more efficient than smoothing via histograms. All in-sample forecasting estimators are collected and their performance is compared via a finite sample simulation study. All methods are extended via multiplicative bias correction. Asymptotic theory is being developed for the histogram-type method of sieves and for the multiplicatively corrected estimators. The multiplicative bias corrected estimators improve all other known in-sample forecasters in the simulation study. The density projection approach seems to have the best performance with forecasting based on survival densities being the runner-up

Obstacles and Challenges to Implementing Multi-departmental QI at a Large, Academic Training Center-Lessons Learned from a HCV Screening Program

Objectives: We aimed to double the HCV screening rate of ‘baby-boomers’ admitted to the medicine teaching service at Methodist Hospital over the course of 6 months and demonstrate improved linkage to care for HCV RNA+ individuals. Initial efforts were a collaboration between Emergency Medicine, where faculty had experience implementing an HIV screening program, and Gastroenterology, a key stakeholder in linkage to care. Our pilot period coincided with new state regulations mandating that hospitals implement HCV screening for inpatients. These new regulations dramatically altered the scope and goals of the project.https://jdc.jefferson.edu/patientsafetyposters/1030/thumbnail.jp

PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers

The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni in the classification setting to more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators. A particular attention is paid to randomized estimators drawn in a small neighborhood of classical estimators, whose study leads to control the risk of the latter. These results allow to bound the risk of very general estimation procedures, as well as to perform model selection

Examining the Impact of the SafeCare Parent-Infant Interaction Module on the Quantity and Content of Maternal-Infant Directed Utterances

Abstract Sanjana S. Mammen Examining the SafeCare Parent-Infant Interaction Module’s Impact on the Quantity and Content of Maternal-Infant Directed Utterances (Under the direction of Shannon Self Brown, PhD) Positive parenting skills reduce risk for child maltreatment. The Parent-Infant Interaction (PII) module of SafeCare was designed to promote positive parent-child relationships; however, little research has examined its impact on parent-infant utterances. Past research has indicated that a rich parent-child language environment predicts literacy skills and academic achievement, so the present research studies how PII impacts positive maternal infant-directed utterances. Three dyads with various risk levels with infants aged younger than 9-months were offered PII training and a short video modeling positive parent-infant communication. Multiple-probe, single-case experimental design yielded data with several positive trends for maternal-infant utterances, but findings were inconsistent during all conditions. Conversely, following the video, improved utterances were demonstrated consistently across all activities and dyads. These pilot data render several future studies relevant to further our understanding of PII’s impact on maternal-infant communication broadly, including more rigorous research designs and measures to further study this important outcome

Hiding solutions in random satisfiability problems: A statistical mechanics approach

A major problem in evaluating stochastic local search algorithms for NP-complete problems is the need for a systematic generation of hard test instances having previously known properties of the optimal solutions. On the basis of statistical mechanics results, we propose random generators of hard and satisfiable instances for the 3-satisfiability problem (3SAT). The design of the hardest problem instances is based on the existence of a first order ferromagnetic phase transition and the glassy nature of excited states. The analytical predictions are corroborated by numerical results obtained from complete as well as stochastic local algorithms.Comment: 5 pages, 4 figures, revised version to app. in PR

Population density, water supply, and the risk of dengue fever in Vietnam: cohort study and spatial analysis.

BACKGROUND: Aedes aegypti, the major vector of dengue viruses, often breeds in water storage containers used by households without tap water supply, and occurs in high numbers even in dense urban areas. We analysed the interaction between human population density and lack of tap water as a cause of dengue fever outbreaks with the aim of identifying geographic areas at highest risk. METHODS AND FINDINGS: We conducted an individual-level cohort study in a population of 75,000 geo-referenced households in Vietnam over the course of two epidemics, on the basis of dengue hospital admissions (n = 3,013). We applied space-time scan statistics and mathematical models to confirm the findings. We identified a surprisingly narrow range of critical human population densities between around 3,000 to 7,000 people/km² prone to dengue outbreaks. In the study area, this population density was typical of villages and some peri-urban areas. Scan statistics showed that areas with a high population density or adequate water supply did not experience severe outbreaks. The risk of dengue was higher in rural than in urban areas, largely explained by lack of piped water supply, and in human population densities more often falling within the critical range. Mathematical modeling suggests that simple assumptions regarding area-level vector/host ratios may explain the occurrence of outbreaks. CONCLUSIONS: Rural areas may contribute at least as much to the dissemination of dengue fever as cities. Improving water supply and vector control in areas with a human population density critical for dengue transmission could increase the efficiency of control efforts. Please see later in the article for the Editors' Summary

A COMPREHENSIVE REVIEW ON COMORBID DEPRESSION IN PATIENTS WITH EPILEPSY

Epilepsy is one of the common neurological disorders that are seen worldwide. It can also affect a person's social, mental, and physiological well-being and thus restricts and disables the common living of man. Depression as such has been well reported in patients with epilepsy, and also depression itself remains a factor that can lead to the development of epilepsy. Increased rates of suicidal tendencies are also associated with depression both in men and women. Depression is further extended in affecting the quality of life in epileptic patients. Depressive rates are found to have a higher value in epileptic patients when compared with the normal population. This article aims to produce a comprehensive review of the epidemiological considerations, clinical findings and management strategies for depression associated with epilepsy

Iron homeostasis: new players, newer insights

Although iron is a relatively abundant element in the universe, it is estimated that more than 2 billion people worldwide suffer from iron deficiency anemia. Iron deficiency results in impaired production of iron-containing proteins, the most prominent of which is hemoglobin. Cellular iron deficiency inhibits cell growth and subsequently leads to cell death. Hemochromatosis, an inherited disorder results in disproportionate absorption of iron and the extra iron builds up in tissues resulting in organ damage. As both iron deficiency and iron overload have adverse effects, cellular and systemic iron homeostasis is critically important. Recent advances in the field of iron metabolism have led to newer understanding of the pathways involved in iron homeostasis and the diseases which arise from alteration in the regulators. Although insight into this complex regulation of the proteins involved in iron homeostasis has been obtained mainly through animal studies, it is most likely that this knowledge can be directly extrapolated to humans

Constructing living buildings: a review of relevant technologies for a novel application of biohybrid robotics

Biohybrid robotics takes an engineering approach to the expansion and exploitation of biological behaviours for application to automated tasks. Here, we identify the construction of living buildings and infrastructure as a high-potential application domain for biohybrid robotics, and review technological advances relevant to its future development. Construction, civil infrastructure maintenance and building occupancy in the last decades have comprised a major portion of economic production, energy consumption and carbon emissions. Integrating biological organisms into automated construction tasks and permanent building components therefore has high potential for impact. Live materials can provide several advantages over standard synthetic construction materials, including self-repair of damage, increase rather than degradation of structural performance over time, resilience to corrosive environments, support of biodiversity, and mitigation of urban heat islands. Here, we review relevant technologies, which are currently disparate. They span robotics, self-organizing systems, artificial life, construction automation, structural engineering, architecture, bioengineering, biomaterials, and molecular and cellular biology. In these disciplines, developments relevant to biohybrid construction and living buildings are in the early stages, and typically are not exchanged between disciplines. We, therefore, consider this review useful to the future development of biohybrid engineering for this highly interdisciplinary application.publishe

A Path Algorithm for Constrained Estimation

Many least squares problems involve affine equality and inequality constraints. Although there are variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current paper proposes a new path following algorithm for quadratic programming based on exact penalization. Similar penalties arise in $l_1$ regularization in model selection. Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to $\infty$, one recovers the constrained solution. In the exact penalty method, squared penalties are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the lasso and generalized lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well chosen examples illustrate the mechanics and potential of path following.Comment: 26 pages, 5 figure