Results 111 to 120 of about 167,791 (166)

From concrete to canopy: Illuminating moth biodiversity in New York City's urban jungle. [PDF]

PLoS One
Linsk S, Thonis A, Winchell KM.
europepmc   +1 more source

The impact of psychological distress on working memory during COVID-19 by disentangling cognitive deficits from emotional burden. [PDF]

Sci Rep
Chiara K, Sabrina T, Michael H, Catherine W.   +3 more
europepmc   +1 more source

Benthic Ecosystem Calcification Measured with Coupled pH and O₂ Aquatic Eddy Covariance. [PDF]

ACS ES T Water
Koopmans D, Schaap A, Meyer V, Färber P, Queiss L, Montilla L, Loucaides S, Ahmerkamp S, Cardini U.   +8 more
europepmc   +1 more source

Two-Dimensional DOA Estimation of Coherent Sources Based on Uniform Linear Electromagnetic Vector Sensor Array. [PDF]

Sensors (Basel)
Zhang J, Lan X, Wang X.
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High-Precision and Efficient Calibration of Robot Polishing Systems Using an Adaptive Residual EKF Optimized by MIPO. [PDF]

Sensors (Basel)
Wang L, Yao Y, Ruan S, Li H, Zhang X, Zhang Y, Zang Z, Yu Z.   +7 more
europepmc   +1 more source

High-Precision Laser Time-Frequency Synchronization in Space Based on an Improved Kalman Filtering Method. [PDF]

Sensors (Basel)
Sun B, Wang X, Sun Z, Zheng F.
europepmc   +1 more source

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Functional Data Analysis with Covariate-Dependent Mean and Covariance Structures

Biometrics, 2022
Abstract Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external
Chenlin Zhang, Huazhen Lin, Li Liu, Jin Liu, Yi Li   +4 more
openaire   +3 more sources

Estimating the covariance function with functional data

British Journal of Mathematical and Statistical Psychology, 2002
This paper describes a two‐step procedure for estimating the covariance function and its eigenvalues and eigenfunctions in situations where the data are curves or functions. The first step produces initial estimates of eigenfunctions using a standard principal components analysis.
Sik-Yum, Lee, Wenyang, Zhang, Xin-Yuan, Song   +2 more
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