Stein’s method of moments for truncated multivariate distributions [PDF]
Electronic Journal of Statistics, 2023 We use Stein characterisations to derive new moment-type estimators for the parameters of several truncated multivariate distributions in the i.i.d. case; we also derive the asymptotic properties of these estimators.
A. Fischer, Robert E. Gaunt, Yvik Swan
semanticscholar +1 more source
Exploring the Intrinsic Probability Distribution for Hyperspectral Anomaly Detection
Remote Sensing, 2022 In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to their powerful reconstruction ability compared with traditional methods.
Shaoqi Yu +3 more
doaj +1 more source
A spatial analysis of multivariate output from regional climate models [PDF]
, 2011 Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output.
Cressie, Noel +2 more
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Limit theorems for linear eigenvalue statistics of overlapping matrices [PDF]
, 2015 The paper proves several limit theorems for linear eigenvalue statistics of overlapping Wigner and sample covariance matrices. It is shown that the covariance of the limiting multivariate Gaussian distribution is diagonalized by choosing the Chebyshev ...
Kargin, Vladislav
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Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics
Remote Sensing, 2019 Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion ...
A. Jaafari, D. Gholami, B. Pham, D. Bui
semanticscholar +1 more source
Tests for covariance matrices, particularly for high dimensional data [PDF]
, 2010 Test statistics for sphericity and identity of the covariance matrix are presented, when the data are multivariate normal and the dimension, p, can be larger than the sample size, n.
Ahmad, M. Rauf
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Generative machine learning methods for multivariate ensemble post-processing [PDF]
Annals of Applied Statistics, 2022 Ensemble weather forecasts based on multiple runs of numerical weather prediction models typically show systematic errors and require post-processing to obtain reliable forecasts. Accurately modeling multivariate dependencies is crucial in many practical
Jieyu Chen +3 more
semanticscholar +1 more source
The Sampling Distributions of Statistics Derived From the Multivariate t-Distribution [PDF]
Australian Journal of Physics, 1955 The sampling distributions of the more important statistical derivates from the multivariate t-distribution are established.
openaire +3 more sources
Optimal and fast detection of spatial clusters with scan statistics [PDF]
, 2010 We consider the detection of multivariate spatial clusters in the Bernoulli model with $N$ locations, where the design distribution has weakly dependent marginals.
Walther, Guenther
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Characterizations of the distribution of the Demmel condition number of real Wishart matrices
Special Matrices, 2016 The Demmel condition number is an indicator of the matrix condition, and its properties have recently found applications in many practical problems, such as in MIMO communication systems, in the analytical prediction of level-crossing and fade duration ...
Shakil M., Ahsanullah M.
doaj +1 more source