| dc.contributor.author |
Sreedevi, E P |
|
| dc.contributor.author |
Dr.Sankaran, P G |
|
| dc.date.accessioned |
2014-05-23T06:13:41Z |
|
| dc.date.available |
2014-05-23T06:13:41Z |
|
| dc.date.issued |
2010-04-09 |
|
| dc.identifier.uri |
http://dyuthi.cusat.ac.in/purl/3810 |
|
| dc.description |
Department of Statistics, Cochin University of Science and
Technology |
en_US |
| dc.description.abstract |
there has been much research
on analyzing various forms of competing risks data. Nevertheless, there are several
occasions in survival studies, where the existing models and methodologies are
inadequate for the analysis competing risks data. ldentifiabilty problem and various
types of and censoring induce more complications in the analysis of competing risks
data than in classical survival analysis. Parametric models are not adequate for the
analysis of competing risks data since the assumptions about the underlying lifetime
distributions may not hold well. Motivated by this, in the present study. we develop
some new inference procedures, which are completely distribution free for the
analysis of competing risks data. |
en_US |
| dc.description.sponsorship |
Cochin University of Science and
Technology |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
Cochin University Of Science And Technology |
en_US |
| dc.subject |
Censoring |
en_US |
| dc.subject |
Truncation |
en_US |
| dc.subject |
Competing Risks Models |
en_US |
| dc.subject |
Neural Network Models for Competing Risks Data |
en_US |
| dc.subject |
Tests for Continuous Lifetime Data |
en_US |
| dc.title |
‘Modeling and Analysis of Competing Risks Data’ |
en_US |
| dc.type |
Thesis |
en_US |