An empirical investigation of the determinants of R&D cooperation: An application of the inverse hyperbolic sine transformation

2012 ◽  
Vol 66 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Oliviero A. Carboni
Keyword(s):  
Empirical Investigation ◽  
Hyperbolic Sine ◽  
Inverse Hyperbolic Sine

Elasticities and the Inverse Hyperbolic Sine Transformation

2019 ◽  
Vol 82 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Marc F. Bellemare ◽  
Casey J. Wichman
Keyword(s):  
Hyperbolic Sine ◽  
Inverse Hyperbolic Sine

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

2021 ◽  
Vol 1 (1) ◽  
pp. 42-51
Author(s):  
Awogbemi C.A. ◽  
Olowu A.R.
Keyword(s):  
Normal Distribution ◽  
Distribution Theory ◽  
Hyperbolic Sine ◽  
Inverse Hyperbolic Sine ◽  
Probability Of Misclassification

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.

Estimation and use of the inverse hyperbolic sine transformation to model non-normal correlated random variables

1994 ◽  
Vol 21 (4) ◽  
pp. 289-304 ◽  
Author(s):  
Octavio A. Ramirez ◽  
Charles B. Moss2 ◽  
William G. Boggess2
Keyword(s):  
Random Variables ◽  
Hyperbolic Sine ◽  
Inverse Hyperbolic Sine

The Role of Wealth Transformations: An Application to Estimating the Effect of Tax Incentives on Saving

2006 ◽  
Vol 5 (1) ◽  
Author(s):  
Karen M. Pence
Keyword(s):  
Tax Incentives ◽  
Household Wealth ◽  
Logarithmic Transformation ◽  
Percentage Change ◽  
Median Regression ◽  
Household Saving ◽  
Hyperbolic Sine ◽  
Corporate Profits ◽  
Inverse Hyperbolic Sine

AbstractResearchers may want to estimate the percentage change of a variable, such as household wealth or corporate profits, that takes on economically significant nonpositive values. Using the logarithmic transformation, however, requires discarding observations with nonpositive values. This paper describes a possible solution to this problem-the inverse hyperbolic sine transformation-and shows how to implement this transformation optimally in the case of median regression. As an illustration of the usefulness of this transformation, I revisit a specification sometimes used to estimate the effect of tax incentives on household saving.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258155
Author(s):  
Sihai Guan ◽  
Qing Cheng ◽  
Yong Zhao ◽  
Bharat Biswal
Keyword(s):  
Adaptive Filtering ◽  
Sine Function ◽  
Hyperbolic Functions ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Impulsive Interference ◽  
Hyperbolic Sine ◽  
Robust Adaptive ◽  
Inverse Hyperbolic Sine ◽  
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.

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