In regression analysis for deriving scaling laws in the context of fusion studies, usually standard regression methods have been applied, of which ordinary least squares (OLS) is the most popular. However, concerns have been raised with respect to several assumptions underlying OLS in its application to fusion data. More sophisticated statistical techniques are available, but they are not widel...

In social movements, the decision of individuals to participate depends both on their own preferences as well as the behavior of others. Three examples of this type of interaction are the Arab Spring protests, Kickstarter campaigns, and internet memes. One way of modeling this is Granovetter’s threshold model of collective action, in which each agent needs to see a “threshold” level of particip...

This paper investigates the impact of government size on economic growth in selected economies of the MENA countries by using a non-linear panel data approach over the period 1990-2011. The estimation results of Panel Smooth Threshold Regression model show that when the level of government consumption is very large, the positive impact of labor force on growth is intensified. On the other hand,...

We consider the problem of updating beliefs for binary random variables, when probability assessments are elicited for them based on information of varying quality. We propose the threshold model, a Bayesian updating procedure where only measures of location and correlation have to be speciied before any updating is possible. The main aspect of this model is the use of Jeerey's conditionalizati...

The present work describes a simple approach to estimating the location of a threshold/change point in a nonparametric regression. This model has connections both to the time-series and regression discontinuity literatures. The estimator leverages a simple decomposition, giving it the form of a semiparametric smooth coefficient model. Optimal bandwidth selection and a suite of testing facilitie...

We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is u...

The open-loop Threshold Model, proposed by Tong [30], is a piecewise-linear stochastic regression model useful for modeling conditionally normal response time-series data. However, in many applications, the response variable is conditionally non-normal, e.g. Poisson or binomially distributed. We generalize the open-loop Threshold Model by introducing the Generalized Threshold Model (GTM). Speci...

Control of the longitudinal profile of ablated structures during laser processing is a key technological requirement. We report here on the direct machining of trenches in silicon with circular profiles using femtosecond accelerating beams. We describe the ablation process based on an intensity threshold model, and show how the depth of the trenches can be predicted in the framework of a causti...

This paper is a selective review of the development of the threshold model in time series analysis over the past 30 years or so. First, it re-visits the motivation of the model. Next, it describes the various expressions of the model, highlighting the principle underlying them and the main probabilistic and statistical properties. Finally, after listing some of the recent offsprings of the thre...

The open-loop Threshold Model, proposed by Tong [23], is a piecewise-linear stochastic regression model useful for modeling conditionally normal response time-series data. However, in many applications, the response variable is conditionally non-normal, e.g. Poisson or binomially distributed. We generalize the open-loop Threshold Model by introducing the Generalized Threshold Model (GTM). Speci...