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As we described in "Background", the tremor signal in the EMG signals is multiplicative noise, and it is difficult to process multiplicative noise with methods for additive noise.
In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated.
Howard et al. (2014) assessed 14 parametric and nonparametric methods using simulated genetic architectures, and found that parametric methods performed slightly better than nonparametric methods for additive genetic architectures, but parametric methods had difficulty in capturing non-additive effects such as epistatic effects.
Distinctions among parametric methods for additive genetic architectures were incremental.
Parametric methods were slightly better than nonparametric methods for additive genetic architectures.
The risk difference estimates could theoretically be obtained with an additive risk model yet, unlike methods for multiplicative modeling, reliable methods for additive risk regression with case control data were not available.
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An active fault diagnosis method for additive, parametric or multiplicative faults is proposed.
This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors.
This paper addresses the generic performance of inspection methods suitable for additive manufacturing.
Further, lexpit regression advances current methods for assessing additive interactions in case control studies.
Estimating and visualizing the joint distribution of ancestry and interclass heterozygosity is an effective way to compare the genetic structure of hybrid populations and these estimates can be used in classic quantitative genetic methods for assessing additive, dominant, and epistatic genetic effects on hybrid phenotypes and fitness.
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