Results 11 to 20 of about 2,452,838 (337)

Gradient boosting for linear mixed models [PDF]

The International Journal of Biostatistics, 2021
Abstract Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by adapting concepts from classification theory. Current boosting approaches also offer methods accounting for random effects and thus enable prediction ...
Griesbach, Colin, Säfken, Benjamin, Waldmann, Elisabeth   +2 more
openaire   +5 more sources

Linear Mixed Model for Genotype Selection of Sorghum Yield

Applied Sciences, 2023
Data analysis using the General linear model assumes the factors to be fixed effects, and the BLUE method, which is based on their mean performance, is appropriate to select the best performing genotypes.
Mulugeta Tesfa, Temesgen Zewotir, Solomon Assefa Derese, Denekew Bitew Belay, Hussein Shimelis   +4 more
doaj   +1 more source

Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship

Journal of Forest Science, 2015
Models of height curves generated using a linear mixed effects model and generalized model were compared. Both tested models were also compared with local models of height curves, which were fitted using a nonlinear regression.
Z. Adamec
doaj   +1 more source

Generalized linear mixed model for segregation distortion analysis

BMC Genetics, 2011
Background Segregation distortion is a phenomenon that the observed genotypic frequencies of a locus fall outside the expected Mendelian segregation ratio. The main cause of segregation distortion is viability selection on linked marker loci.
Zhan Haimao, Xu Shizhong
doaj   +1 more source

A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence [PDF]

, 2015
A bivariate copula mixed model has been recently proposed to synthesize diagnostic test accuracy studies and it has been shown that it is superior to the standard generalized linear mixed model in this context.
Aas K, Aristidis K Nikoloulopoulos, Brenner H, Chu H, Chu H, Demidenko E, Genest C, Genest C, Genest C, Hamza TH, Hoyer A, Hua L, Hult H, Joe H, Joe H, Joe H, Joe H, Joe H, Karageorgopoulos DE, Kurowicka D, Kuss O, Leeflang MMG, Leeflang MMG, Ma X, Mavridis D, McNeil AJ, Nash JC, Nelsen RB, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Nikoloulopoulos AK, Panagiotelis A, Paul M, Serfling RJ, Shen C, Stroud AH, Vuong QH, Ye Y   +41 more
core   +2 more sources

Functional Linear Mixed Models for Irregularly or Sparsely Sampled Data [PDF]

, 2015
We propose an estimation approach to analyse correlated functional data which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model.
Cederbaum, Jona, Greven, Sonja, Hoole, Phil, Pouplier, Marianne   +3 more
core   +2 more sources

Monitoring of Linear Profiles Using Linear Mixed Model in the Presence of Measurement Errors

Mathematics, 2022
In the application of control charts, most of the research in profile monitoring is based on accurate measurements. Measurement errors, however, often exist in many manufacturing and service environments.
Wenhui Liu, Zhonghua Li, Zhaojun Wang
doaj   +1 more source

Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales - A case study for Masson pine in Southern China

Journal of Forest Science, 2012
The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models.
L.Y. Fu, W.S. Zeng, S.Z. Tang, R.P. Sharma, H.K. Li   +4 more
doaj   +1 more source

Modified BIC Criterion for Model Selection in Linear Mixed Models

Mathematics, 2023
Linear mixed-effects models are widely used in applications to analyze clustered, hierarchical, and longitudinal data. Model selection in linear mixed models is more challenging than that of linear models as the parameter vector in a linear mixed model ...
Hang Lai, Xin Gao
doaj   +1 more source

A mixed integer linear programming model for minimum backbone grid

Frontiers in Energy Research, 2023
Developing a minimum backbone grid in the power system planning is beneficial to improve the power system’s resilience. To obtain a minimum backbone grid, a mixed integer linear programming (MILP) model with network connectivity constraints for a minimum
Wenwen Mei, Zhiyuan Sun, Yuanjian He, Mosi Liu, Xianfu Gong, Peijie Li   +5 more
doaj   +1 more source