Sentence examples for marginal regression from inspiring English sources
We propose and analyze a novel framework for learning sparse representations based on two statistical techniques: kernel smoothing and marginal regression.
Furthermore, we propose using marginal regression for obtaining sparse codes which significantly improves the speed and allows one to scale to large dictionary sizes easily.
In this paper we study properties of the estimators of marginal mean parameters in the GEE1 approach of Heagerty and Zeger [Heagerty, P.J., Zeger, S.L., 1996. Marginal regression models for clustered ordinal measurements. J. Amer. Statist. Assoc. 91, 1024 1036] for clustered ordinal data.
Time-dependent sensitivities of MGIT and MODS by days 7 and 14, respectively, were compared using a marginal regression model as described above.
Han and Pan [17] addressed this first fitting a marginal regression model for the association between the variant and disease, and then flipping the coding of the genotype when the estimated coefficient is negative and reaches a certain significance threshold.
a marginal regression model (fitted using standard maximum likelihood) that ignores clustering ('No clustering').
In this situation, only marginal regressions in tumour volume are caused by the highest doses of the drug.
The marginal logistic regression model used to describe the data found a significant (p < 0.05) association of herd size cow-origin interaction.
In such cases, marginal mean regression parameters in MZIP, MZINB, MPois-Pois and MNB-Pois models have straightforward interpretations in describing overall exposure effects on count outcomes.
Recently, marginal mean regression modeling procedures for zero-inflated count outcomes have been introduced within the framework of maximum likelihood estimation of zero-inflated Poisson and negative binomial regression models.
Overall, the simulation results indicate that when the true model is specified, MPois-Pois or MNB-Pois models estimate marginal mean regression parameters with small biases, Type I errors close to the assumed rate and coverages of 95% confidence intervals near 95% for sample sizes of 200 or greater.
