Likelihood-Ratio-Based Inference is a statistical inference method that uses the ratio of maximized likelihoods under nested models to assess the relative fit of competing hypotheses, distinguishing itself by relying on the likelihood ratio as the primary basis for hypothesis testing and model comparison.
144
Publications
13.4K
Citations
316
Authors
186
Institutions
Publications per year
2017–2026
12
Parents
Publications
144
1–5 of 144
Authors
316
- FJ
4
h-index
- CA
4
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- JC
3
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- OE
2
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- SL
2
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1–5 of 316
Institutions
186
Stanford University
Stanford, United States
4 h-index
4 publications
2.4K citations
University of Waterloo
Waterloo, Canada
4 h-index
4 publications
127 citations
University of California, Los Angeles
Los Angeles, United States
4 h-index
6 publications
433 citations
University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
13 publications
559 citations
Universidade Nova de Lisboa
Lisbon, Portugal
4 h-index
8 publications
216 citations
York University
Toronto, Canada
3 h-index
4 publications
119 citations
University of Liège
Liège, Belgium
3 h-index
6 publications
294 citations
Royal Holloway University of London
Egham, United Kingdom
3 h-index
3 publications
260 citations
1–8 of 186
Venues
- Biometrika
20 publications · 3.1K citations
18
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- The Annals of Statistics
6 publications · 639 citations
6
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- Computational Statistics & Data Analysis
CSDA · 5 publications · 162 citations
5
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- Journal of Econometrics
4 publications · 391 citations
4
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- The Annals of Mathematical Statistics
4 publications · 3.5K citations
4
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1–5