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kdecopula: An R Package for the Kernel Estimation of Bivariate Copula Densities
Journal of Statistical Software, 2018 We describe the R package kdecopula (current version 0.9.2), which provides fast implementations of various kernel estimators for the copula density. Due to a variety of available plotting options it is particularly useful for the exploratory analysis of
Thomas Nagler
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Canadian Journal of Statistics, EarlyView.Abstract This article presents a strategy for conducting regression analysis of zero‐truncated recurrent event data. The research is partly motivated by a pediatric mental health care (PMHC) program based on administrative data. We are particularly interested in how the occurrence of an event depends on its past occurrences and the associated ...
Anqi A. Chen +3 more
wiley +1 more source
Vine copula knockoffs for variable selection in gene expression studies
Canadian Journal of Statistics, EarlyView.Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina +3 more
wiley +1 more source
Clinical Pharmacology &Therapeutics, EarlyView.Regulatory agencies request the assessment of the potential of new drugs to cause transporter‐mediated drug‐drug interactions (DDI). This assessment can be improved by integrating endogenous biomarkers during drug development. Regarding the renal excretion of drugs such as metformin, the organic cation transporter (OCT) 2 and multidrug and toxin ...
Jana Picurová +5 more
wiley +1 more source
Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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Ensemble Estimation of Information Divergence †
Entropy, 2018 Recent work has focused on the problem of nonparametric estimation of information divergence functionals between two continuous random variables. Many existing approaches require either restrictive assumptions about the density support set or difficult ...
Kevin R. Moon +3 more
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Multivariate Density Estimation and Visualization [PDF]
This chapter examines the use of flexible methods to approximate an unknown density function, and techniques appropriate for visualization of densities in up to four dimensions. The statistical analysis of data is a multilayered endeavor.
Scott, David W.
core
Deep Underground Science and Engineering, EarlyView.Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
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Earthquake Engineering &Structural Dynamics, EarlyView.ABSTRACT Seismic fragility assessment of reinforced concrete (RC) bridges exposed to corrosive environments must reliably capture time‐dependent deterioration impacts. Climate change compounds this challenge through nonstationary temperature and humidity variations that accelerate bridge corrosion.
Yexiang Yan, Yazhou Xie
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Kernel Density Estimation: Theory and Application in Discriminant Analysis
Austrian Journal of Statistics, 2016 Nowadays, one can find a huge set of methods to estimate the density function of a random variable nonparametrically. Since the first version of the most elementary nonparametric density estimator (the histogram) researchers produced a vast amount of ...
Thomas Ledl
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