Statisticshttp://dyuthi.cusat.ac.in:80/xmlui/handle/purl/4952016-05-06T03:36:51Z2016-05-06T03:36:51ZProduct autoregressive models for non-negative variablesBalakrishna, NAbraham, Bhttp://dyuthi.cusat.ac.in:80/xmlui/handle/purl/47252014-09-23T20:30:21Z2012-05-07T00:00:00ZProduct autoregressive models for non-negative variables
Balakrishna, N; Abraham, B
When variables in time series context are non-negative, such as for volatility, survival
time or wave heights, a multiplicative autoregressive model of the type Xt = Xα
t−1Vt ,
0 ≤ α < 1, t = 1, 2, . . . may give the preferred dependent structure. In this paper,
we study the properties of such models and propose methods for parameter estimation.
Explicit solutions of the model are obtained in the case of gamma marginal distribution
Statistics and Probability Letters 82 (2012) 1530–1537
2012-05-07T00:00:00ZSome Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal OperationsJames, K Chttp://dyuthi.cusat.ac.in:80/xmlui/handle/purl/46662014-08-21T20:30:22Z2012-10-01T00:00:00ZSome Aspects of Domain Specific Conceptual Modeling for Simulation of Logistic Terminal Operations
James, K C
When simulation modeling is used for
performance improvement studies of complex systems such as
transport terminals, domain specific conceptual modeling
constructs could be used by modelers to create structured
models. A two stage procedure which includes identification of
the problem characteristics/cluster - ‘knowledge acquisition’
and identification of standard models for the problem cluster –
‘model abstraction’ was found to be effective in creating
structured models when applied to certain logistic terminal
systems. In this paper we discuss some methods and examples
related the knowledge acquisition and model abstraction stages
for the development of three different types of model categories
of terminal systems
Proceedings of the World Congress on Engineering and Computer Science 2012 Vol II
WCECS 2012, October 24-26, 2012, San Francisco, USA
2012-10-01T00:00:00ZQuantile based stop-loss transform and its applicationsSunoj, S MUnnikrishnan Nair, NSankaran, P Ghttp://dyuthi.cusat.ac.in:80/xmlui/handle/purl/42902014-07-25T20:30:33Z2012-09-29T00:00:00ZQuantile based stop-loss transform and its applications
Sunoj, S M; Unnikrishnan Nair, N; Sankaran, P G
Partial moments are extensively used in actuarial science for the analysis
of risks. Since the first order partial moments provide the expected loss in a stop-loss
treaty with infinite cover as a function of priority, it is referred as the stop-loss transform.
In the present work, we discuss distributional and geometric properties of the
first and second order partial moments defined in terms of quantile function. Relationships
of the scaled stop-loss transform curve with the Lorenz, Gini, Bonferroni and
Leinkuhler curves are developed
Stat Methods Appl (2013) 22:167–182
DOI 10.1007/s10260-012-0213-4
2012-09-29T00:00:00ZQuantile based reliability aspects of partial momentsSunoj, S MUnnikrishnan Nair, NSankaran, P Ghttp://dyuthi.cusat.ac.in:80/xmlui/handle/purl/42892014-07-25T20:30:32Z2012-12-01T00:00:00ZQuantile based reliability aspects of partial moments
Sunoj, S M; Unnikrishnan Nair, N; Sankaran, P G
Partial moments are extensively used in literature for modeling and analysis of lifetime
data. In this paper, we study properties of partial moments using quantile functions.
The quantile based measure determines the underlying distribution uniquely. We then
characterize certain lifetime quantile function models. The proposed measure provides
alternate definitions for ageing criteria. Finally, we explore the utility of the measure to
compare the characteristics of two lifetime distributions
Journal of the Korean Statistical Society 42 (2013) 329–342
2012-12-01T00:00:00Z