Effect size is becoming an increasingly popular measure of the importance of an effect, both in individual studies and in meta-analyses. However, a large effect size is not the only way to demonstrate that an effect is important. This article describes 2 alternative methodological strategies, in which importance is a function of how minimal a manipulation of the independent variable or how diff...

In this paper we introduce some new methods using which importance of a variable in a Boolean function can be computed. Such methods can easily be used for computing variable order to construct BDDs. The measures of a variable's importance are based on information-theoretic criteria, and require computation of entropy of a variable, as deened by us, for a given function. We have found that entr...

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is dev...

A search is described for valid methods of assessing the importance of explanatory variables in logistic regression, motivated by earlier work on the relationship between corporate governance variables and the issuance of restricted voting shares (RSF). The methods explored are adaptations of Pratt’s (1987) approach for measuring variable importance in simple linear regression, which is based o...

Among the various methods so far proposed for genetic network inference, this study focuses on random-forest-based methods. Confidence values are assigned to all of candidate regulations when taking approach. To our knowledge, make assignments using standard variable importance measure defined in tree-based machine learning techniques. Therefore, sum confidence a certain gene from other genes, ...