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Estimation Methods for Mixed Logistic Models with Few Clusters
Daniel McNeish
Research output
:
Contribution to journal
›
Article
›
peer-review
25
Scopus citations
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Dive into the research topics of 'Estimation Methods for Mixed Logistic Models with Few Clusters'. Together they form a unique fingerprint.
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Mathematics
Logistic Model
100%
Mixed Model
98%
Biased
82%
Likelihood
75%
Adaptive Quadrature
69%
Laplace Approximation
59%
Restricted Maximum Likelihood
57%
Gaussian Quadrature
55%
Binary Outcomes
54%
Linearly
34%
Restricted Estimation
33%
Empirical Analysis
30%
Estimate
30%
Data Modeling
28%
Linearization Method
27%
Number of Clusters
27%
Linear Mixed Model
27%
Fixed Effects
25%
Variance Components
24%
Closed-form Solution
23%
Standard error
22%
Approximation
22%
Small Sample
21%
Maximum Likelihood Estimation
20%
Linearization
19%
Model
14%
Analogue
14%
Simulation
13%
Arts & Humanities
Approximation
89%
Linearization
63%
Maximum Likelihood Estimation
40%
Fixed Effects
38%
Maximum Likelihood
33%
Small Sample
29%
Modeling
18%
Simulation
14%
Social Sciences
logistics
67%
simulation
17%
lack
11%
trend
10%
Medicine & Life Sciences
Logistic Models
57%
Linear Models
17%