Probabilities of Misclassification Linked with Inverse Hyperbolic Sine Normal (IHSN) Distribution

Probability of misclassification occurs when there is a choice of criteria that is not favourable for classification. The probabilities of misclassification associated with a family of Johnson’s system, the Inverse Hyperbolic Sine Normal distribution, was developed in this study. The distribution theory and rules, along with the formulation of the system, were generated. It was asserted that the estimation of the parameters of the system could be demystified if one or more variables under consideration are distributed normally.

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function

Recently, adaptive filtering algorithms were designed using hyperbolic functions, such as hyperbolic cosine and tangent function. However, most of those algorithms have few parameters that need to be set, and the adaptive estimation accuracy and convergence performance can be improved further. More importantly, the hyperbolic sine function has not been discussed. In this paper, a family of adaptive filtering algorithms is proposed using hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF) function. Specifically, development of a robust adaptive filtering algorithm based on HSF, and extend the HSF algorithm to another novel adaptive filtering algorithm based on IHSF; then continue to analyze the computational complexity for HSF and IHSF; finally, validation of the analyses and superiority of the proposed algorithm via simulations. The HSF and IHSF algorithms can attain superior steady-state performance and stronger robustness in impulsive interference than several existing algorithms for different system identification scenarios, under Gaussian noise and impulsive interference, demonstrate the superior performance achieved by HSF and IHSF over existing adaptive filtering algorithms with different hyperbolic functions.

European FDI in Ireland and Iceland: Before and after the Financial Crisis

This paper analyses Foreign Direct Investment (FDI) investment in Ireland and Iceland from other European countries during two periods, i.e., the pre-financial crisis period of 2000–2007 and the financial crisis period of 2008–2010. The aim of this research is to determine what made the countries interesting to foreign investors in both good and bad times; and, secondly, to examine whether European Union membership (and the Euro) made a difference in this respect. The results were obtained by using data from the OECD, the World bank, and other sources. The model constructed for the study applies the inverse hyperbolic sine transformation of the gravity model, which is a novel approach. The results demonstrate that before the financial crisis of 2008, European Union (EU) membership did not help Ireland attract more FDI from other EU countries. However, once it had been hit by the crisis, Ireland attracted more FDI from other EU countries. Iceland, on the other hand, which is not an EU country, attracted FDI from non-EU countries rather than from EU countries before the financial crisis. After the crisis, however, the origin within Europe, of FDI in Iceland had no significant effect on the flow of FDI into the country.

Units of measurement and the inverse hyperbolic sine transformation

Summary The inverse hyperbolic sine (IHS) transformation is frequently applied in econometric studies to transform right-skewed variables that include zero or negative values. We show that regression results can heavily depend on the units of measurement of IHS-transformed variables. Hence, arbitrary choices regarding the units of measurement for these variables can have a considerable effect on recommendations for policies or business decisions. In order to address this problem, we suggest a procedure for choosing units of measurement for IHS-transformed variables. A Monte Carlo simulation assesses this procedure under various scenarios, and an empirical illustration shows the relevance and applicability of our suggested procedure.

VSS: Variance-stabilized signals for sequencing-based genomic signals

MotivationA sequencing-based genomic assay such as ChIP-seq outputs a real-valued signal for each position in the genome that measures the strength of activity at that position. Most genomic signals lack the property of variance stabilization. That is, a difference between 100 and 200 reads usually has a very different statistical importance from a difference between 1,100 and 1,200 reads. A statistical model such as a negative binomial distribution can account for this pattern, but learning these models is computationally challenging. Therefore, many applications—including imputation and segmentation and genome annotation (SAGA)—instead use Gaussian models and use a transformation such as log or inverse hyperbolic sine (asinh) to stabilize variance.ResultsWe show here that existing transformations do not fully stabilize variance in genomic data sets. To solve this issue, we propose VSS, a method that produces variance-stabilized signals for sequencingbased genomic signals. VSS learns the empirical relationship between the mean and variance of a given signal data set and produces transformed signals that normalize for this dependence. We show that VSS successfully stabilizes variance and that doing so improves downstream applications such as SAGA. VSS will eliminate the need for downstream methods to implement complex mean-variance relationship models, and will enable genomic signals to be easily understood by [email protected]://github.com/faezeh-bayat/Variance-stabilized-units-for-sequencing-based-genomic-signals.