The importance of ideas: an a priori critical juncture framework

This paper sets out an improved framework for examining critical junctures. This framework, while rigorous and broadly applicable and an advance on the frameworks currently employed, primarily seeks to incorporate an a priori element. Until now the frameworks utilized in examining critical junctures were entirely postdictive. Adding a predictive element to the concept will constitute a significant advance. The new framework, and its predictive element, termed the “differentiating factor,” is tested here in examining macro-economic crises and subsequent changes in macro-economic policy, in America and Sweden

Taking the Impact Factor seriously is similar to taking creationism, homeopathy or divining seriously

There is no evidence that journal rank has any persuasive predictive property for any measure of scientific quality. Every scientist who is not aware of the unscientific nature of the Impact Factor should ask themselves if they are in the right profession, writes Bjoern Brembs

Gut-Derived Serum Lipopolysaccharide is Associated With Enhanced Risk of Major Adverse Cardiovascular Events in Atrial Fibrillation: Effect of Adherence to Mediterranean Diet

Gut microbiota is emerging as a novel risk factor for atherothrombosis, but the predictive role of gut-derived lipopolysaccharide (LPS) is unknown. We analyzed (1) the association between LPS and major adverse cardiovascular events (MACE) in atrial fibrillation (AF) and (2) its relationship with adherence to a Mediterranean diet (Med-diet)

Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models

This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, the authors first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, they construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of their tests, the authors also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.Econometric models - Evaluation ; Stochastic analysis

Biomarkers in ocular chronic graft versus host disease: tear cytokine- and chemokine-based predictive model.

Producción CientíficaPurpose: To develop a tear molecule level-based predictive model based on a panel of tear cytokines and their correlation with clinical features in ocular chronic graft versus host disease (cGVHD). Methods: Twenty-two ocular cGVHD patients and 21 healthy subjects were evaluated in a controlled environmental research laboratory (CERLab). Clinical parameters were recorded, and tears were collected. Levels of 15 molecules (epidermal growth factor [EGF], IL receptor antagonist [IL-1Ra], IL-1β, IL-2, IL-6, IL-8/CXCL8, IL-10, IL-12p70, IL-17A, interferon inducible protein [IP]-10/CXCL10, IFN-γ, VEGF, TNF-α, eotaxin 1, and regulated on activation normal T cell expressed and secreted [RANTES]) were measured by multiplex-bead assay and correlated with clinical parameters. Logistic regression was used to develop a predictive model. Leave-one-out cross-validation was applied. Classification capacity was evaluated in a cohort of individuals with dry eye (DE) of other etiologies different from GVHD. Results: Epidermal growth factor and IP-10/CXCL10 levels were significantly decreased in ocular cGVHD, positively correlating with tear production and stability and negatively correlating with symptoms, hyperemia, and vital staining. Interleukin-1Ra, IL-8/CXCL8, and IL-10 were significantly increased in ocular cGVHD, and the first two correlated positively with symptoms, hyperemia, and ocular surface integrity while negatively correlating with tear production and stability. Predictive models were generated, and the best panel was based on IL-8/CXCL8 and IP-10/CXCL10 tear levels along with age and sex, with an area under the receiving operating curve of 0.9004, sensitivity of 86.36%, and specificity of 95.24%. Conclusions: A predictive model based on tear levels of IL-8/CXCL8 and IP-10/CXCL10 resulted in optimal sensitivity and specificity. These results add further knowledge to the search for potential biomarkers in this devastating ocular inflammatory disease.Ministry of Economy and Competitiveness, Madrid, Spain, SAF-2010 15631 (AES)

Technical note: Bias and the quantification of stability

Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias

Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach

We use factor augmented predictive regressions to investigate the relationship between excess bond returns and the macro economy. Our application is for the case of United Kingdom. The dimension of the large data set with 127 variables is reduced by the method of principal components and the Onatski (2009) procedure is used to determine the number factors. Our data covers the period 1983:09 - 2006:10. We find that variation in the one year ahead excess returns on 2 to 5-year UK government bonds can be modeled by macroeconomic fundamentals with R-square values varying from 34 percent to 44 percent. Specifically, three macro factors "unemployment" factor, "inflation" factor and "stock market" factor have significant predictive power in explaining the variation in the excess bond returns. Our results provide new evidence against the expectations hypothesis for the case of UK. We contribute to the literature by analyzing the direct link between macroeconomic variables and excess bond returns for a European market rather than the US. Unpredictability of excess bond returns is not the case in the UK either.Principal Components Analysis (PCA); Expectations Hypothesis; Excess Bond Returns; Factor Models.

Electron-impact ionization of atomic hydrogen at 2 eV above threshold

The convergent close-coupling method is applied to the calculation of fully differential cross sections for ionization of atomic hydrogen by 15.6 eV electrons. We find that even at this low energy the method is able to yield predictive results with small uncertainty. As a consequence we suspect that the experimental normalization at this energy is approximately a factor of two too high.Comment: 10 page

Forecasting with Medium and Large Bayesian VARs

This paper is motivated by the recent interest in the use of Bayesian VARs for forecasting, even in cases where the number of dependent variables is large. In such cases, factor methods have been traditionally used but recent work using a particular prior suggests that Bayesian VAR methods can forecast better. In this paper, we consider a range of alternative priors which have been used with small VARs, discuss the issues which arise when they are used with medium and large VARs and examine their forecast performance using a US macroeconomic data set containing 168 variables. We ?nd that Bayesian VARs do tend to forecast better than factor methods and provide an extensive comparison of the strengths and weaknesses of various approaches. Our empirical results show the importance of using forecast metrics which use the entire predictive density, instead of using only point forecasts.Bayesian, Minnesota prior, stochastic search variable selection, predictive likelihood

Correlation energy contribution to nuclear masses

The ground state correlation energies associated with collective surface and pairing vibrations are calculated for Pb- and Ca-isotopes. It is shown that this contribution, when added to those predicted by one of the most accurate modern nuclear mass formula (HFBCS MSk7 mass formula), reduces the associated rms error by an important factor, making mean field theory, once its time dependence is taken into account, a quantitative predictive tool for nuclear masses.Comment: 4 pages, 2 figures, RevTeX