Automated lip reading from videos requires segmentation. Threshold-based segmentation is straightforward, but it rarely used. This study proposes a histogram threshold based on the feedback of shape information. Both good and bad examples were used to train an $$\epsilon $$ -support vector regression model infer accuracy region shape. The was optimised minimise error. proposed method tested 895...

This paper studies control function (CF) approaches in endogenous threshold regression where the variable is allowed to be endogenous. We first use a simple example show that structural (STR) estimator of point Kourtellos, Stengos and Tan (2016, Econometric Theory 32, 827–860) inconsistent unless endogeneity level low compared effect. correct CF STR generate our using method extends two-stage l...

In regression discontinuity models, where the probability of treatment jumps discretely when a running variable crosses a threshold, an average treatment effect can be nonparametrically identi ed. We show that the derivative of this treatment effect with respect to the threshold is also nonparametrically identi ed and easily estimated, in both sharp and fuzzy designs. This marginal threshold tr...

In this article, we develop a general method for testing threshold effects in regression models, using sup-likelihood-ratio (LR)-type statistics. Although the sup-LR-type test statistic has been considered in the literature, our method for establishing the asymptotic null distribution is new and nonstandard. The standard approach in the literature for obtaining the asymptotic null distribution ...

First hitting time models are a technique of modeling a stochastic process as it approaches or avoids a boundary, also known as a threshold. The process itself may be unobservable, making this a difficult problem. Regression techniques, however, can be employed to model the data as it compares to the threshold, creating a class of first hitting time models called threshold regression models. Su...

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...