Results 81 to 90 of about 12,807,756 (320)
Sampling with discrete contamination [PDF]
, 2011 The sampling variance for a process stream which carries fluctuating levels of the sought-after analyte and is subject to mass flow variation can be estimated from the covariance function of the analyte fluctuation and the covariance function of the ...
Bourgeois, Florent, Lyman, Geoffrey
core
Multivariate Covariance Generalized Linear Models
, 2015 We propose a general framework for non-normal multivariate data analysis called multivariate covariance generalized linear models (McGLMs), designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation ...
Bonat, Wagner Hugo, Jørgensen, Bent
core +1 more source
Arthritis Care &Research, EarlyView.Objective Hydroxychloroquine (HCQ) is a cornerstone therapy in systemic lupus erythematosus (SLE), but the weight‐based dosing does not account for clinical factors that can introduce individual variability in drug metabolism and clearance. We leveraged longitudinal data from a prospective SLE cohort to identify clinical factors that predict ...
Jay J. Patel +6 more
wiley +1 more source
Discriminant analysis of Gaussian spatial data with exponential covariance structure
Lietuvos Matematikos Rinkinys, 2005 This paper considers the discrimination of the observation of the stationary Gaussian random field belonging to one of two populations with different means and covariance functions.
Kęstutis Dučinskas
doaj +3 more sources
LÉVY-BASED ERROR PREDICTION IN CIRCULAR SYSTEMATIC SAMPLING
Image Analysis and Stereology, 2013 In the present paper, Lévy-based error prediction in circular systematic sampling is developed. A model-based statistical setting as in Hobolth and Jensen (2002) is used, but the assumption that the measurement function is Gaussian is relaxed.
Kristjana Ýr Jónsdóttir +1 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Linear discriminant analysis of spatial Gaussian data with estimated anisotropy ratio
Lietuvos Matematikos Rinkinys, 2011 The paper deals with a problem of classification of Gaussian spatial data into one of two populations specified by different parametric mean models and common geometric anisotropic covariance function.
Lina Dreižienė
doaj +1 more source
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova +2 more
wiley +1 more source