Results 11 to 20 of about 301,037 (257)
Study of Bayesian variable selection method on mixed linear regression models.
PLoS ONE, 2023 Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model ...
Yong Li, Hefei Liu, Rubing Li
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Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression [PDF]
Journal of Statistical Software, 2014 Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a ?exible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or
Marco Geraci
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Insight into Genome-Wide Associations of Growth Trajectories Using a Hierarchical Non-Linear Mixed Model [PDF]
BiologyIn applying a hierarchical mixed model to genome-wide association analysis (GWAS) of longitudinal data, dimensionality reduction through modeling repeated measurements improves both computational efficiency and statistical power. Legendre polynomials can
Ying Zhang +3 more
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The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist [PDF]
, 2020 This text provides a brief and accessible guide for implementing general, ANOVA and linear mixed models for the analysis of real world data from chemistry, industrial, bio or life-sciences experiments. The reader is introduced to the main concepts and vocabulary of the subject and to the main statistical methods available for formalizing, estimating ...
Govaerts, Bernadette +4 more
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Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models [PDF]
2019 IEEE 58th Conference on Decision and Control (CDC), 2019 We consider {\em Mixed Linear Regression (MLR)}, where training data have been generated from a mixture of distinct linear models (or clusters) and we seek to identify the corresponding coefficient vectors. We introduce a {\em Mixed Integer Programming (MIP)} formulation for MLR subject to regularization constraints on the coefficient vectors.
Taiyao Wang, Ioannis Ch. Paschalidis
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Estimating functional linear mixed-effects regression models [PDF]
Computational Statistics & Data Analysis, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Baisen Liu +2 more
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Brain Sciences, 2017 Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants.
Tess K. Koerner, Yang Zhang
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On the mixed Kibria–Lukman estimator for the linear regression model
Scientific Reports, 2022 AbstractThis paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS estimator, mixed estimator and Kibria–Lukman estimator as special cases. In addition, we discuss the
Hongmei Chen, Jibo Wu
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Scientific Reports, 2021 Repeated measures studies are frequently performed in patient-derived xenograft (PDX) models to evaluate drug activity or compare effectiveness of cancer treatment regimens. Linear mixed effects regression models were used to perform statistical modeling
Ann L. Oberg +13 more
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Remote Sensing, 2022 Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite ...
Schyler Brown +2 more
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