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In addition, the quantile regression approach has its advantages over traditional regression models for its flexibility to allow the covariates to have different impacts at different percentiles of the distribution as well as its robustness with respect to departures from normality and skewed tails because it does not put any distributional assumption beforehand (18– 20).
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Since the Bayesian analysis of flexible mixtures of (shifted) Poisson distributions allowed us to estimate the posterior probability without the necessity of making assumptions beforehand, it is a promising method to implement into future studies on frequencies of CNVs or gene expression.
Our filtering method could inform the likely difference in intensity since the probes furthest away from the optimal hybridisation temperature could be identified beforehand and assumptions about their ability to hybridise could be built into the design a priori.
Moreover, most assume the burst time is known beforehand, an unrealistic assumption in practical implementations.
Beforehand, the linear assumption was assessed in univariate regression models and was considered satisfactory.
Unlike previous work, this method does not require assumption of the flux distribution beforehand.
The fact that some managers do indeed deliver leads us to the false and very dangerous assumption that we can identify them beforehand.
Both [6, 7] are different from the GIA in that they are both based on the assumption that source precoders are given beforehand.
The simulation was conducted under the assumption that the arachnoid mater was dissected beforehand.
First, with the assumption that all energy and data arrivals are known beforehand, i.e., the offline setup, we identify the optimal transmission policies.
As has been mentioned in the assumptions, we assume that the split rates are obtained beforehand.
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