Modelling credit spreads with time volatility, skewness, and kurtosis

This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Growth and nutritional status of children with homozygous sickle cell disease

Background: Poor growth and under-nutrition are common in children with sickle cell disease (SCD). This review summarises evidence of nutritional status in children with SCD in relation to anthropometric status, disease severity, body composition, energy metabolism, micronutrient deficiency and endocrine dysfunction. Methods: A literature search was conducted on the Medline/PUBMED, SCOPUS, SciELO and LILACS databases to July 2007 using the keywords sickle cell combined with nutrition, anthropometry, growth, height and weight, body mass index, and specific named micronutrients. Results: Forty-six studies (26 cross-sectional and 20 longitudinal) were included in the final anthropometric analysis. Fourteen of the longitudinal studies were conducted in North America, the Caribbean or Europe, representing 78.8% (2086/2645) of patients. Most studies were observational with wide variations in sample size and selection of reference growth data, which limited comparability. There was a paucity of studies from Africa and the Arabian Peninsula, highlighting a large knowledge gap for low-resource settings. There was a consistent pattern of growth failure among affected children from all geographic areas, with good evidence linking growth failure to endocrine dysfunction, metabolic derangement and specific nutrient deficiencies. Conclusions: The monitoring of growth and nutritional status in children with SCD is an essential requirement for comprehensive care, facilitating early diagnosis of growth failure and nutritional intervention. Randomised controlled trials are necessary to assess the potential benefits of nutritional interventions in relation to growth, nutritional status and the pathophysiology of the disease