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Quantile regression for survival data in modern cancer research: expanding statistical tools for precision medicine
Quantile regression links the whole distribution of an outcome to the covariates of interest
and has become an important alternative to commonly used regression models. However …
and has become an important alternative to commonly used regression models. However …
Residuals and diagnostics for ordinal regression models: a surrogate approach
Ordinal outcomes are common in scientific research and everyday practice, and we often
rely on regression models to make inference. A long-standing problem with such regression …
rely on regression models to make inference. A long-standing problem with such regression …
Mid-quantile regression for discrete responses
We develop quantile regression methods for discrete responses by extending Parzen's
definition of marginal mid-quantiles. As opposed to existing approaches, which are based …
definition of marginal mid-quantiles. As opposed to existing approaches, which are based …
Bayesian quantile regression for ordinal models
MA Rahman - 2016 - projecteuclid.org
The paper introduces a Bayesian estimation method for quantile regression in univariate
ordinal models. Two algorithms are presented that utilize the latent variable inferential …
ordinal models. Two algorithms are presented that utilize the latent variable inferential …
Bayesian quantile regression for ordinal longitudinal data
Since the pioneering work by Koenker and Bassett [27], quantile regression models and its
applications have become increasingly popular and important for research in many areas. In …
applications have become increasingly popular and important for research in many areas. In …
Assessing partial association between ordinal variables: quantification, visualization, and hypothesis testing
Partial association refers to the relationship between variables Y 1, Y 2,…, YK while
adjusting for a set of covariates X={X 1,…, X p}. To assess such an association when Yk's …
adjusting for a set of covariates X={X 1,…, X p}. To assess such an association when Yk's …
[کتاب][B] Distributed computing and monitoring technologies for older patients
J Klonovs, MA Haque, V Krueger, K Nasrollahi… - 2015 - Springer
In this book, we summarize recently deployed monitoring approaches with a focus on
automatically detecting health threats for older patients living alone at home. First, in order to …
automatically detecting health threats for older patients living alone at home. First, in order to …
Bayesian model selection in ordinal quantile regression
A Bayesian stochastic search variable selection (BSSVS) method is presented for variable
selection in quantile regression (QReg) for ordinal models. A Markov Chain Monte Carlo …
selection in quantile regression (QReg) for ordinal models. A Markov Chain Monte Carlo …
Design-based conformal prediction
Conformal prediction is an assumption-lean approach to generating distribution-free
prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite …
prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite …
Tackling ordinal regression problem for heterogeneous data: sparse and deep multi-task learning approaches
Many real-world datasets are labeled with natural orders, ie, ordinal labels. Ordinal
regression is a method to predict ordinal labels that finds a wide range of applications in …
regression is a method to predict ordinal labels that finds a wide range of applications in …