The concept of a nested canalizing Boolean function has been studied over the course of the last decade in the context of understanding the regulatory logic of molecular interaction networks, such as gene regulatory networks. Such functions appear preferentially in published models of such networks. Recently, this concept has been generalized to include multi-state functions, and a recursive fo...

The aim of this study is to examine the equity premium puzzle in Iran for the quarterly period of 1993-2016. In this regard, the hybrid bivariate Garch model and also fuzzy dummy variables with consumption capital asset pricing model (C-CAPM) have been used. The results of study show that using C-CAPM within fuzzy dummy variables (CCAPM-F), the relative risk aversion coefficient of investor is ...

Consider the problem of estimating true density, h(.) based upon a random sample X1,…, Xn. In general, h(.)is approximated using an appropriate in some sense, see below) model fƟ(x). This article using Vuong's (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(.).Application of such confide...

Ecologists often seek to understand patterns and processes across multiple spatial and temporal scales ranging from centimeters to hundreds of meters and from seconds to years. Hierarchical statistical models offer a framework for sampling design and analysis that can be used to incorporate the information collected at finer scales while allowing comparison at coarser scales. In this study we u...

The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatialmodel (GLSMs)was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality.Modelling the influence of these variables and analysing the spatial distribution of themod...

Experimental neuroscience increasingly requires tractable models for analyzing and predicting the behavior of neurons and networks. The generalized linear model (GLM) is an increasingly popular statistical framework for analyzing neural data that is flexible, exhibits rich dynamic behavior and is computationally tractable (Paninski, 2004; Pillow et al., 2008; Truccolo et al., 2005). What follow...