Parametric Density Estimation
Parametric Density Estimation is a density estimation method that assumes a fixed functional form for the probability density function, characterized by a finite number of parameters, and estimates these parameters from the data to model the underlying distribution.
137
Publications
10.4K
Citations
277
Authors
167
Institutions
Publications per year
2017–2026
14
137
Minimum Hellinger Distance Estimates for Parametric Models
Rudolf Beran · The Annals of Statistics · 1977 · 756 citations · Full text
Latent Space Autoregression for Novelty Detection
Davide Abati, Angelo Porrello, Simone Calderara et al. · 2019 · 502 citations · Full text
Locally parametric nonparametric density estimation
Nils Lid Hjort, M. C. Jones · The Annals of Statistics · 1996 · 323 citations · Full text
1–5 of 137
277
University of North Carolina at Chapel Hill, United States
6 publications · 677 citations
6
h-index
4
h-index
London School of Economics and Political Science, United Kingdom
3 publications · 188 citations
3
h-index
3
h-index
2
h-index
1–5 of 277
167
University of North Carolina at Chapel Hill
Chapel Hill, United States
6 h-index
10 publications
733 citations
Stanford University
Stanford, United States
6 h-index
8 publications
729 citations

Cornell University
Ithaca, United States
4 h-index
4 publications
640 citations
University of Wisconsin–Madison
Madison, United States
3 h-index
4 publications
189 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
4 publications
108 citations

Monash University
Melbourne, Australia
3 h-index
4 publications
189 citations
University of Michigan
Ann Arbor, United States
3 h-index
3 publications
308 citations

University of Oslo
Oslo, Norway
3 h-index
6 publications
834 citations
1–8 of 167
14 publications · 966 citations
13
h-index
11 publications · 2.2K citations
11
h-index
8 publications · 236 citations
8
h-index
JSCS · 4 publications · 186 citations
4
h-index
Repository · 4 publications · 81 citations
4
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1–5