scholarly journals Using Linear Mixed-Effects Models with Quantile Regression to Simulate the Crown Profile of Planted Pinus sylvestris var. Mongolica Trees

Forests ◽  
2017 ◽  
Vol 8 (11) ◽  
pp. 446 ◽  
Author(s):  
Yunxia Sun ◽  
Huilin Gao ◽  
Fengri Li
Keyword(s):  
Pinus Sylvestris ◽  
Quantile Regression ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features

2017 ◽  
Vol 28 (2) ◽  
pp. 569-588 ◽  
Author(s):  
Hanze Zhang ◽  
Yangxin Huang ◽  
Wei Wang ◽  
Henian Chen ◽  
Barbara Langland-Orban
Keyword(s):  
Viral Load ◽  
Longitudinal Data ◽  
Quantile Regression ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Asymmetric Distribution ◽  
Joint Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Partially Linear

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

2021 ◽  
pp. 001316442199489
Author(s):  
Luyao Peng ◽  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Aggregate Level ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Best Linear Unbiased

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

scholarly journals Association of body condition with lameness in dairy cattle: a single-farm longitudinal study

2021 ◽  
pp. 1-4
Author(s):  
Michaela Kranepuhl ◽  
Detlef May ◽  
Edna Hillmann ◽  
Lorenz Gygax
Keyword(s):  
Body Condition ◽  
Body Condition Score ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Confounding Variables ◽  
Condition Score ◽  
The Impact ◽  
The Relationship

Abstract This research communication describes the relationship between the occurrence of lameness and body condition score (BCS) in a sample of 288 cows from a single farm that were repeatedly scored in the course of 9 months while controlling for confounding variables. The relationship between BCS and lameness was evaluated using generalised linear mixed-effects models. It was found that the proportion of lame cows was higher with decreasing but also with increasing BCS, increased with lactation number and decreased with time since the last claw trimming. This is likely to reflect the importance of sufficient body condition in the prevention of lameness but also raises the question of the impact of overcondition on lameness and the influence of claw trimming events on the assessment of lameness. A stronger focus on BCS might allow improved management of lameness that is still one of the major problems in housed cows.

D-optimal cohort designs for linear mixed-effects models

2007 ◽  
Vol 27 (14) ◽  
pp. 2586-2600 ◽  
Author(s):  
Fetene B. Tekle ◽  
Frans E. S. Tan ◽  
Martijn P. F. Berger
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects
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