Results 31 to 40 of about 162 (71)
Multiple tests of association with biological annotation metadata
, 2008 We propose a general and formal statistical framework for multiple tests of association between known fixed features of a genome and unknown parameters of the distribution of variable features of this genome in a population of interest.
Mark J. Van Der Laan +4 more
core +2 more sources
Estimating the parameters of a dependent model and applying it to environmental data set. [PDF]
J Appl Stat, 2023 Mohtashami-Borzadaran V +2 more
europepmc +1 more source
Quantile association regression on bivariate survival data. [PDF]
Can J Stat, 2021 Chen LW, Cheng Y, Ding Y, Li R.
europepmc +1 more source
ASYMPTOTIC DISTRIBUTIONS OF HIGH-DIMENSIONAL DISTANCE CORRELATION INFERENCE. [PDF]
Ann Stat, 2021 Gao L, Fan Y, Lv J, Shao QM.
europepmc +1 more source
Oscillating neural circuits: Phase, amplitude, and the complex normal distribution. [PDF]
Can J Stat, 2023 Urban KN, Bong H, Orellana J, Kass RE.
europepmc +1 more source
Using statistical smoothing to date medieval manuscripts
, 2008 We discuss the use of multivariate kernel smoothing methods to date manuscripts dating from the 11th to the 15th centuries, in the English county of Essex. The dataset consists of some 3300 dated and 5000 undated manuscripts, and the former are used as a
Feuerverger, Andrey +3 more
core +1 more source
Asymptotic distributions of robust shape matrices and scales [PDF]
It has been frequently observed in the literature that many multivariate statistical methods require the covariance or dispersion matrix ∑ of an elliptical distribution only up to some scaling constant.
Frahm, Gabriel
core
Multivariate Tail Coefficients: Properties and Estimation. [PDF]
Entropy (Basel), 2020 Gijbels I, Kika V, Omelka M.
europepmc +1 more source
A generalization of Tyler's M-estimators to the case of incomplete data [PDF]
Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established until today. Tyler's M-estimator has been recognized as the 'most robust' M-estimator for the shape matrix of elliptically symmetric
Frahm, Gabriel, Jaekel, Uwe
core