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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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The Elasticity of a Random Variable as a Tool for Measuring and Assessing Risks
Risks, 2022 Elasticity is a very popular concept in economics and physics, recently exported and reinterpreted in the statistical field, where it has given form to the so-called elasticity function.
Ernesto-Jesús Veres-Ferrer +1 more
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Journal of Mathematical Analysis and Applications, 1986 In a previous paper [ibid. 91, 552-558 (1983; Zbl 0528.54009)] the authors defined differentials for a fuzzy valued function. In this clearly thought and well-written paper they define an integral, or an expected value, of a fuzzy valued random variable. The definition is based on the integral of a set-valued function.
Puri, Madan L, Ralescu, Dan A
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, 2020 The probability density function (PDF) of a random variable associated with the solution of a partial differential equation (PDE) with random parameters is approximated using a truncated series expansion.
Capodaglio, Giacomo +2 more
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An extension to the Wiener space of the arbitrary functions principle [PDF]
, 2006 The arbitrary functions principle says that the fractional part of $nX$ converges stably to an independent random variable uniformly distributed on the unit interval, as soon as the random variable $X$ possesses a density or a characteristic function ...
Bouleau, Nicolas
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Инженерно-строительный журнал, 2015 The article proposes the method of calculating the bearing capacity of reinforced concrete beams at the operational stage by the rigidity (deflection) criterion. The methods, which were used in the article, are integral test and probabilistic methods for
V.S. Utkin, S.A. Solovyov
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Binary and Ordinal Random Effects Models Including Variable Selection [PDF]
, 2010 A likelihood-based boosting approach for fitting binary and ordinal mixed models is presented. In contrast to common procedures it can be used in high-dimensional settings where a large number of potentially influential explanatory variables is available.
Groll, Andreas, Tutz, Gerhard
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Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
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Conditional variable importance for random forests
BMC Bioinformatics, 2008 Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables.
Augustin Thomas +4 more
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Random variable functions used in hydrology
Selecciones Matemáticas, 2019 In this work, expressions of the cumulative distribution function of Y X, Y/X and X/(X + Y ) for continuous dependent random variables with supported on a unbounded and bounded interval are derived.
Cira E. G. Otiniano, Yuri S. Maluf
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