Results 91 to 100 of about 3,287,971 (240)

Expected Logarithm and Negative Integer Moments of a Noncentral χ²-Distributed Random Variable. [PDF]

Entropy (Basel), 2020
Moser SM.
europepmc   +1 more source

ivcrc: An Instrumental Variables Estimator for the Correlated Random Coefficients Model

, 2022
David A. Benson, Matthew A. Masten, Alexander Torgovitsky   +2 more
openalex   +1 more source

Measurement protocols, random-variable-valued measurements, and response process error: Estimation and inference when sample data are not deterministic. [PDF]

PLoS One, 2020
Kroc E.
europepmc   +1 more source

Modeling Directional Monotonicity in Sequence with Copulas

Axioms
In this paper, we present the concept of being monotonic in sequence according to a specific direction for a collection of random variables. This concept broadens the existing notions of multivariate dependence, such as sequential left-tail and right ...
José Juan Quesada-Molina, Manuel Úbeda-Flores   +1 more
doaj   +1 more source

A Novel Gamma Distributed Random Variable (RV) Generation Method for Clutter Simulation with Non-Integral Shape Parameters. [PDF]

Sensors (Basel), 2020
Chen S, Luo F, Hu C.
europepmc   +1 more source

Prediction of random variables by excursion metric projections [PDF]

, 2022
Vitalii Makogin, Evgeny Spodarev
openalex   +1 more source

A uniform L1 law of large numbers for functions of i.i.d. random variables that are translated by a consistent estimator [PDF]

, 2018
Pierre Lafaye de Micheaux, Frédéric Ouimet   +1 more
openalex   +1 more source

Complete convergence and complete moment convergence for maximal weighted sums of extended negatively dependent random variables under sub-linear expectations

, 2023
Mingzhou Xu
openalex   +1 more source

Process Variable Importance Analysis by Use of Random Forests in a Shapley Regression Framework [PDF]

, 2020
Chris Aldrich
openalex   +1 more source

Upper and lower bounds for sums of random variables. [PDF]

In this contribution, the upper bounds for sums of dependent random variables X1 + X2 + … + X n derived by using comonotonicity are sharpened for the case when there exists a random variable Z such that the distribution functions of the Xi, given Z = z ...
Dhaene, Jan, Goovaerts, Marc, Kaas, R
core