We study an optimal nonparametric regression model for a threshold detector exposed to a noisy, subthreshold signal. The problem of recovering the signal is similar to that faced by neurons in nervous systems, although our model is intended to be normative rather than realistic. In our approach, the time-integrating activity of the neuron is modeled by kernel regression. Several aspects of the ...

It has been hypothesized that number of road accidents and road casualties are increased in line with the raise in the variables of registered vehicles, population and road length. However, the effects of these variables toward road accidents are still inconclusive. Therefore, this paper develops models based on the variables which can be used to determine road accidents in Malaysia. In order t...

We investigate the problem of estimating the quality of the output of machine translation systems at the sentence level when reference translations are not available. The focus is on automatically identifying a threshold to map a continuous predicted score into “good” / “bad” categories for filtering out bad-quality cases in a translation post-edition task. We use the theory of Inductive Confid...

We consider the estimation of parameters in a particular segmented generalized linear model with additive measurement error in predictors, with a focus on linear and logistic regression. In epidemiologic studies segmented regression models often occur as threshold models, where it is assumed that the exposure has no influence on the response up to a possibly unknown threshold. Furthermore, in o...

This paper develops a uniform test of linearity against thresholds effects in the quantile regression framework. The test is based on the supremum of the Wald process over the space of quantile and threshold parameters. We establish the asymptotic null distribution of the test statistic for stationary weakly dependent processes, and propose a simulation method to approximate the critical values...

This paper investigates the nonlinear relationship between capital structure and firm performance in MENA region using a sample of 499 listed firms over 2007–2020 period, or 6986 firm-year observations. Specifically, we examine size-threshold effect structure–firm nexus. To do so, this study applies dynamic panel threshold regression model (DPTR). The findings show that there is debt (Tobin’s Q...

In conventional 3D-CT image processing, the images are influenced by subjective threshold settings. The purpose of this study was to evaluate an objective threshold setting technique based on the discriminant analysis method. The concentration of contrast medium in a joint of a simulated upper cervical spine phantom was changed, and its threshold was measured from scanned data by using the disc...

We use p–values as a discrepancy criterion for identifying the threshold value at which a regression function takes off from its baseline value – a problem that is motivated by applications in omics experiments, systems engineering, pharmacological dose-response studies and astronomy. In this paper, we study the problem in a controlled sampling setting, where multiple responses, discrete or con...

The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous inferences. Furthermore, methods for trying to alleviate such problems, such as univariate generalized linear models, have not provided an adequate...