Two Bayesian/frequentist challenges for categorical data analyses

Alan Agresti ()

METRON, 2014, vol. 72, issue 2, 125-132

Abstract: We discuss two challenging scenarios for frequentist and/or Bayesian inference for categorical data. First, for parameter space regions described by order restrictions, frequentist methods are less straightforward than Bayesian methods, especially for interval estimation. Second, for marginal modeling, frequentist inference is currently feasible only in relatively simplistic settings and Bayesian solutions seem to be essentially non-existent. Both areas have substantial scope for future research. Copyright Sapienza Università di Roma 2014

Keywords: Marginal models; Order-restricted inference; Ordered categorical data; Stochastic orderings (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1007/s40300-014-0036-1 (text/html)
Access to full text is restricted to subscribers.

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:spr:metron:v:72:y:2014:i:2:p:125-132

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40300

DOI: 10.1007/s40300-014-0036-1

Access Statistics for this article

METRON is currently edited by Marco Alfo'

More articles in METRON from Springer, Sapienza Università di Roma
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().