Nowcasting with Google Trends : a keyword selection method

Search engines, such as Google, keep a log of searches entered into their websites. Google makes this data publicly available with Google Trends in the form of aggregate weekly search term volume. Aggregate search volume has been shown to be able to nowcast (i.e. compute real-time assessment of current activity) a variety of variables such as influenza outbreaks, financial market fluctuations, unemployment and retail sales. Although identifying appropriate keywords in Google Trends is an essential element of using search data, the recurring difficulty identified in the literature is the lack of a technique to do so. Given this, the main goal of this paper is to put forward a method (the "backward induction method") of identifying and extracting keywords from Google Trends relevant to economic variables

High performance computation of landscape genomic models integrating local indices of spatial association

Since its introduction, landscape genomics has developed quickly with the increasing availability of both molecular and topo-climatic data. The current challenges of the field mainly involve processing large numbers of models and disentangling selection from demography. Several methods address the latter, either by estimating a neutral model from population structure or by inferring simultaneously environmental and demographic effects. Here we present Sam$\beta$ada, an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Specifically, candidate loci to adaptation are identified by automatically assessing genome-environment associations. In complement, measuring the Local Indicators of Spatial Association (LISA) for these candidate loci allows to detect whether similar genotypes tend to gather in space, which constitutes a useful indication of the possible kinship relationship between individuals. In this paper, we also analyze SNP data from Ugandan cattle to detect signatures of local adaptation with Sam$\beta$ada, BayEnv, LFMM and an outlier method (FDIST approach in Arlequin) and compare their results. Sam$\beta$ada is an open source software for Windows, Linux and MacOS X available at \url{http://lasig.epfl.ch/sambada}Comment: 1 figure in text, 1 figure in supplementary material The structure of the article was modified and some explanations were updated. The methods and results presented are the same as in the previous versio

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Significance Regression: Robust Regression for Collinear Data

This paper examines robust linear multivariable regression from collinear data. A brief review of M-estimators discusses the strengths of this approach for tolerating outliers and/or perturbations in the error distributions. The review reveals that M-estimation may be unreliable if the data exhibit collinearity. Next, significance regression (SR) is discussed. SR is a successful method for treating collinearity but is not robust. A new significance regression algorithm for the weighted-least-squares error criterion (SR-WLS) is developed. Using the weights computed via M-estimation with the SR-WLS algorithm yields an effective method that robustly mollifies collinearity problems. Numerical examples illustrate the main points

What is the evidence of the impact of microfinance on the well-being of poor people?

The concept of microcredit was first introduced in Bangladesh by Nobel Peace Prize winner Muhammad Yunus. Professor Yunus started Grameen Bank (GB) more than 30 years ago with the aim of reducing poverty by providing small loans to the country’s rural poor (Yunus 1999). Microcredit has evolved over the years and does not only provide credit to the poor, but also now spans a myriad of other services including savings, insurance, remittances and non-financial services such as financial literacy training and skills development programmes; microcredit is now referred to as microfinance (Armendáriz de Aghion and Morduch 2005, 2010). A key feature of microfinance has been the targeting of women on the grounds that, compared to men, they perform better as clients of microfinance institutions and that their participation has more desirable development outcomes (Pitt and Khandker 1998). Despite the apparent success and popularity of microfinance, no clear evidence yet exists that microfinance programmes have positive impacts (Armendáriz de Aghion and Morduch 2005, 2010; and many others). There have been four major reviews examining impacts of microfinance (Sebstad and Chen, 1996; Gaile and Foster 1996, Goldberg 2005, Odell 2010, see also Orso 2011). These reviews concluded that, while anecdotes and other inspiring stories (such as Todd 1996) purported to show that microfinance can make a real difference in the lives of those served, rigorous quantitative evidence on the nature, magnitude and balance of microfinance impact is still scarce and inconclusive (Armendáriz de Aghion and Morduch 2005, 2010). Overall, it is widely acknowledged that no well-known study robustly shows any strong impacts of microfinance (Armendáriz de Aghion and Morduch 2005, p199-230). Because of the growth of the microfinance industry and the attention the sector has received from policy makers, donors and private investors in recent years, existing microfinance impact evaluations need to be re-investigated; the robustness of claims that microfinance successfully alleviates poverty and empowers women must be scrutinised more carefully. Hence, this review revisits the evidence of microfinance evaluations focusing on the technical challenges of conducting rigorous microfinance impact evaluations