Sentence examples for model that corrects from inspiring English sources

A new study shows that data generated using the Hi-C approach contain hidden features of interchromosomal DNA interactions, which are revealed through analysis with an integrated probabilistic model that corrects for multiple sources of bias in the data.

Substituting Eq. 4 into Eq. 7, the autoregressive non-linear model that corrects for temporal correlation is: Open image in new window (8).

Using a Prais-Winsten regression model that corrects for autocorrelation in time-series data, and holding constant three leading structural covariates of homicide, this Article finds a large, statistically significant, and robust relationship between aggregated institutionalization and homicide rates.

Using a Prais-Winsten regression model that corrects for autocorrelation in time-series data, and holding constant three leading structural covariates of homicide, this paper finds a large, statistically significant, and robust relationship between aggregated institutionalization and homicide.

We combined propensity score matching with a stochastic production frontier model that corrects sample selection bias resulting from unobserved factors that potentially affect both households' decision to participate in off-farm activities and technical efficiency scores that most previous studies do not account for.

For the parametric specification of the vignette adjustment procedure we use the Compound Hierarchical Ordered Probit (CHOPIT) model which was first applied to vignettes by King et al. ([2004]), and is an extension of the ordered probit model that corrects for DIF.

Analysis was conducted using spatial stochastic frontier models that correct for heterogeneity and spatial interactions between sub-national units.

Linear mixed models that correct for confounding by the genetic background using a kinship matrix calculated from genetic data were used throughout (Kang et al., 2010; Segura et al., 2012).

In future, it may be feasible to develop universal models that correct for sensitivity/specificity differentials of the methods used to test for infection, preferably based on large-scale population surveys which have used both RDT and microscopy for the same individuals with the appropriate quality assurance and external validity.

Understanding the accuracy of self-reported data and developing prediction models that correct for underreporting of hypertension in self-reported data can be critical tools in the development of more accurate population level estimates, and in planning population-based interventions to reduce the risk of, or more effectively treat, hypertension.

Assessing the validity of self-reported data in estimating hypertension prevalence in specific geographic areas, and developing simple prediction models that correct for possible miss reporting of HBP in self-reported data, can be essential to the creation of accurate population level estimates, and for population level efforts to effectively prevent or treat HBP within particular contexts.