Modeling Probability Density Functions as Data Objects
Alexander Petersen,
Chao Zhang and
Piotr Kokoszka
Econometrics and Statistics, 2022, vol. 21, issue C, 159-178
Abstract:
Recent developments in the probabilistic and statistical analysis of probability density functions are reviewed. Density functions are treated as data objects for which suitable notions of the center of distribution and variability are discussed. Special attention is given to nonlinear methods that respect the constraints density functions must obey. Regression, time series and spatial models are discussed. The exposition is illustrated with data examples. A supplementary vignette contains expanded versions of data analyses with accompanying codes.
Keywords: Object-oriented statistics; Probability density functions (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S245230622100054X
Full text for ScienceDirect subscribers only. Contains open access articles
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:21:y:2022:i:c:p:159-178
DOI: 10.1016/j.ecosta.2021.04.004
Access Statistics for this article
Econometrics and Statistics is currently edited by E.J. Kontoghiorghes, H. Van Dijk and A.M. Colubi
More articles in Econometrics and Statistics from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).