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A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions
Aaron J. Molstad
,
Adam J. Rothman
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
2
Scopus citations
Overview
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Dive into the research topics of 'A Likelihood-Based Approach for Multivariate Categorical Response Regression in High Dimensions'. Together they form a unique fingerprint.
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Mathematics
Categorical
100%
Higher Dimensions
97%
Likelihood
92%
Regression
81%
Predictors
73%
Marginal Distribution
54%
Regression Model
43%
Prediction
42%
Interpretability
35%
Penalized Likelihood
33%
Estimator
31%
Odds Ratio
31%
Likelihood Methods
30%
Cancer
27%
Error Bounds
23%
Efficient Algorithms
22%
High-dimensional
21%
Simulation Study
18%
Demonstrate
15%
Performance
15%
Generalization
13%
Estimate
12%
Business & Economics
Predictors
85%
Regression Model
43%
Estimator
42%
Error Bounds
38%
Odds Ratio
35%
Prediction Accuracy
32%
Cancer
31%
Simulation Study
25%
Prediction
21%
Usefulness
21%
Performance
10%