dc.contributor.author |
Asha Gopalakrishnan |
|
dc.contributor.author |
Dr.Unnikrishnan Nair, N |
|
dc.date.accessioned |
2014-04-28T06:19:55Z |
|
dc.date.available |
2014-04-28T06:19:55Z |
|
dc.date.issued |
1995-05-15 |
|
dc.identifier.uri |
http://dyuthi.cusat.ac.in/purl/3696 |
|
dc.description |
Division of Statistics, School of Mathematical Sciences,
Cochin University of Science and Technology |
en_US |
dc.description.abstract |
The term reliability of an equipment or device is
often meant to indicate the probability that it carries out
the functions expected of it adequately or without failure
and within specified performance limits at a given age for a
desired mission time when put to use under the designated
application and operating environmental stress. A broad
classification of the approaches employed in relation to
reliability studies can be made as probabilistic and
deterministic, where the main interest in the former is to
device tools and methods to identify the random mechanism
governing the failure process through a proper statistical
frame work, while the latter addresses the question of
finding the causes of failure and steps to reduce individual
failures thereby enhancing reliability. In the
probabilistic attitude to which the present study subscribes to, the concept of life distribution, a mathematical
idealisation that describes the failure times, is
fundamental and a basic question a reliability analyst has
to settle is the form of the life distribution. It is for
no other reason that a major share of the literature on the
mathematical theory of reliability is focussed on methods of
arriving at reasonable models of failure times and in
showing the failure patterns that induce such models. The
application of the methodology of life time distributions is
not confined to the assesment of endurance of equipments and
systems only, but ranges over a wide variety of scientific
investigations where the word life time may not refer to the
length of life in the literal sense, but can be concieved in
its most general form as a non-negative random variable.
Thus the tools developed in connection with modelling life
time data have found applications in other areas of research
such as actuarial science, engineering, biomedical sciences,
economics, extreme value theory etc. |
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 |
Reliability modelling |
en_US |
dc.subject |
Scalar failure |
en_US |
dc.subject |
Vector failure rate |
en_US |
dc.subject |
Distribution theory. |
en_US |
dc.title |
Some bivariate life time models in discrete time" |
en_US |
dc.type |
Thesis |
en_US |