2024-03-28T23:09:11Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/229172021-06-23T10:10:38Zcom_10324_1151com_10324_931com_10324_894col_10324_1279
Some advances in constrained inference for ordered circular parameters in oscillatory systems
Rueda Sabater, María Cristina
Fernández Temprano, Miguel Alejandro
Barragán, Sandra
Peddada, Shyamal
Constraints on parameters arise naturally in many applications. Statistical methods that
honor the underlying constraints tend to be more powerful and result in better interpretation
of the underlying scientific data. In the context of Euclidean space data, there exists
over five decades of statistical literature on constrained statistical inference and at least four
books on the subject (e.g. Robertson et al. 1988; Silvapulle and Sen 2005). However, it was
not until recently that these methods have been used extensively in applied research. For
example, constrained statistical inference is gaining considerable interest among applied
researchers in a variety of fields, such as, for example, toxicology (Peddada et al. 2007),
genomics (Hoenerhoff et al. 2013; Perdivara et al. 2011; Peddada et al. 2003), epidemiology
(Cao et al. 2011; Peddada et al. 2005), clinical trials (Conaway et al. 2004), or cancer
trials (Conde et al. 2012, 2013).
2017-03-31T08:57:26Z
2017-03-31T08:57:26Z
2015
info:eu-repo/semantics/bookPart
Dryden, Kent (coords). Geometry Driven Statistics. Wiley 2015, p. 97-114.
http://uvadoc.uva.es/handle/10324/22917
eng
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118866576.html
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Wiley
Attribution-NonCommercial-NoDerivatives 4.0 International
Wiley