Comment: Diagnostics and Kernel-based Extensions for Linear Mixed Effects Models with Endogenous Covariates

2020 ◽  
Vol 35 (3) ◽  
pp. 396-399
Author(s):  
Hunyong Cho ◽  
Joshua P. Zitovsky ◽  
Xinyi Li ◽  
Minxin Lu ◽  
Kushal Shah ◽  
...  
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Endogenous Covariates

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

scholarly journals Testing multiple variance components in linear mixed-effects models

Biostatistics ◽  
2012 ◽  
Vol 14 (1) ◽  
pp. 144-159 ◽  
Author(s):  
R. Drikvandi ◽  
G. Verbeke ◽  
A. Khodadadi ◽  
V. Partovi Nia
Keyword(s):  
Variance Components ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects
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