Fan et al. (Ann Stat 47(6):3009–3031, 2019) constructed a distributed principal component analysis (PCA) algorithm to reduce the communication cost between multiple servers significantly. However, their algorithm’s guarantee is only for sub-Gaussian data. Spurred by this deficiency, paper enhances effectiveness of PCA utilizing robust covariance matrix estimators Minsker 46(6A):2871–2903, 2018)...

Authors: Johanna m. de Kort* , Conor V. Dolan*,** and Dorret I. boomsma** In the classical twin study, genetic and environmental influences on a phenotype are usually estimated under the assumption that genotype-environment covariance (Ge covariance) is absent. We explore possibilities to accommodate Ge covariance in longitudinal data using the genetic simplex model. First, the genetic simplex ...

Hybrid ensemble–three-dimensional variational analysis schemes incorporate flow-dependent, ensembleestimated background-error covariances into the three-dimensional variational data assimilation (3DVAR) framework. Typically the 3DVAR background-error covariance estimate is assumed to be stationary, nearly homogeneous, and isotropic. A hybrid scheme can be achieved by 1) directly replacing the b...

An iterative geostatistical inverse approach is developed to estimate conditional effective unsaturated hydraulic conductivity parameters, soil-water pressure head, and degree of saturation in heterogeneous vadose zones. This approach is similar to the classical cokriging technique, and it uses a linear estimator that depends on covariances and cross covariances of unsaturated hydraulic paramet...

We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matérn process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and co-located correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance fun...

For longitudinal data, when the within-subject covariance is misspecified, the semiparametric regression estimator may be inefficient. We propose a method that combines the efficient semiparametric estimator with nonparametric covariance estimation, and is robust against misspecification of covariance models. We show that kernel covariance estimation provides uniformly consistent estimators for...

Principal component analysis (PCA) is a dimensionality reduction method in data that involves diagonalizing the covariance matrix of dataset. Recently, quantum algorithms have been formulated for PCA based on density matrix. These assume can be encoded matrix, but concrete protocol this encoding has lacking. Our work aims to address gap. Assuming amplitude data, with given by ensemble {pi,|ψi⟩}...