A bivariate semi-Pareto distribution is introduced
and characterized using geometric minimization.
Autoregressive minification models for bivariate random
vectors with bivariate semi-Pareto and bivariate Pareto
distributions are also discussed. Multivariate
generalizations of the distributions and the processes are briefly indicated.
This paper proposes different estimators for the parameters of SemiPareto and Pareto autoregressive minification processes The asymptotic properties of the estimators are established by showing that the SemiPareto process is α-mixing. Asymptotic variances of different moment and maximum likelihood estimators are compared.
Sunoj, S M; Unnikrishnan Nair, N(Calcutta: The Association,[1947]-, June , 1999)
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In this paper, a family of bivariate distributions whose marginals are weighted
distributions in the original variables is studied. The relationship between the failure rates
of the derived and original models are obtained. These relationships are used to provide
some characterizations of specific bivariate models
Description:
Bulletin of the Calcutta Statistical Association,Vol 57 (227-228),pp 179-194
In this paper we try to fit a threshold autoregressive (TAR) model to time series data of monthly coconut oil prices at Cochin market. The procedure proposed by Tsay [7] for fitting the TAR model is briefly presented. The fitted model is compared with a simple autoregressive (AR) model. The results are in favour of TAR process. Thus the monthly coconut oil prices exhibit a type of non-linearity which can be accounted for by a threshold model.
Sunoj, S M; Sankaran, P G(Springer, August 6, 2002)
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In this paper, we study the relationship between the failure rate and the
mean residual life of doubly truncated random variables. Accordingly, we
develop characterizations for exponential, Pareto 11 and beta distributions.
Further, we generalize the identities for fire Pearson and the exponential
family of distributions given respectively in Nair and Sankaran (1991) and
Consul (1995). Applications of these measures in file context of lengthbiased
models are also explored
Sunoj, S M; Unnikrishnan Nair, N(Taylor & Francis, August 24, 2002)
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In this paper the class of continuous bivariate distributions that has form-invariant weighted distribution with weight
function w(x1, x2) ¼ xa1
1 xa2
2 is identified. It is shown that the class includes some well known bivariate models.
Bayesian inference on the parameters of the class is considered and it is shown that there exist natural conjugate
priors for the parameters
In this article it is proved that the stationary Markov sequences generated by minification models are ergodic and uniformly mixing. These results are used to establish the optimal properties of estimators for the parameters in the model. The problem of estimating the parameters in the exponential minification model is discussed in detail.
Inthis paper,we define partial moments for a univariate continuous random
variable. A recurrence relationship for the Pearson curve using the partial moments is
established. The interrelationship between the partial moments and other reliability
measures such as failure rate, mean residual life function are proved. We also prove
some characterization theorems using the partial moments in the context of length
biased models and equilibrium distributions
Description:
METRON - International Journal of Statistics
2004, vol. LXII, n. 3, pp. 353-362
This paper presents gamma stochastic volatility models and investigates its distributional
and time series properties. The parameter estimators obtained by the
method of moments are shown analytically to be consistent and asymptotically
normal. The simulation results indicate that the estimators behave well. The insample
analysis shows that return models with gamma autoregressive stochastic
volatility processes capture the leptokurtic nature of return distributions and
the slowly decaying autocorrelation functions of squared stock index returns
for the USA and UK. In comparison with GARCH and EGARCH models, the
gamma autoregressive model picks up the persistence in volatility for the US
and UK index returns but not the volatility persistence for the Canadian and
Japanese index returns. The out-of-sample analysis indicates that the gamma
autoregressive model has a superior volatility forecasting performance compared
to GARCH and EGARCH models.
Sunoj, S M; Maya, S S(Taylor & Francis, August 19, 2006)
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In this article we introduce some structural relationships between weighted and
original variables in the context of maintainability function and reversed repair rate.
Furthermore, we prove some characterization theorems for specific models such as
power, exponential, Pareto II, beta, and Pearson system of distributions using the
relationships between the original and weighted random variables
Description:
Communications in Statistics—Theory and Methods, 35: 223–228, 2006
Sunoj, S M; Sankaran, P G; Maya, S S(Taylor & Francis, December 20, 2006)
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In this paper, we examine the relationships between log odds rate and various reliability measures
such as hazard rate and reversed hazard rate in the context of repairable systems. We also prove
characterization theorems for some families of distributions viz. Burr, Pearson and log exponential
models. We discuss the properties and applications of log odds rate in weighted models. Further we
extend the concept to the bivariate set up and study its properties.
In this paper, we study some dynamic generalized information measures between a
true distribution and an observed (weighted) distribution, useful in life length studies. Further,
some bounds and inequalities related to these measures are also studied
Lower partial moments plays an important role in the analysis of risks and
in income/poverty studies. In the present paper, we further investigate its importance
in stochastic modeling and prove some characterization theorems arising out of it. We
also identify its relationships with other important applied models such as weighted
and equilibrium models. Finally, some applications of lower partial moments in
poverty studies are also examined
Description:
METRON - International Journal of Statistics
2008, vol. LXVI, n. 2, pp. 223-242
Sunoj, S M; Sankaran, P G; Maya, S S(Taylor & Francis, September 5, 2008)
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In this article, we study reliability measures such as geometric vitality function
and conditional Shannon’s measures of uncertainty proposed by Ebrahimi (1996)
and Sankaran and Gupta (1999), respectively, for the doubly (interval) truncated
random variables. In survival analysis and reliability engineering, these measures
play a significant role in studying the various characteristics of a system/component
when it fails between two time points. The interrelationships among these uncertainty
measures for various distributions are derived and proved characterization theorems
arising out of them
Description:
Communications in Statistics—Theory and Methods, 38: 1441–1452, 2009
The average availability of a repairable system is the expected proportion of time that the system is operating in the interval [0, t]. The present article discusses the nonparametric estimation of the average availability when (i) the data on 'n' complete cycles of system operation are available, (ii) the data are subject to right censorship, and (iii) the process is observed upto a specified time 'T'. In each case, a nonparametric confidence interval for the average availability is also constructed. Simulations are conducted to assess the performance of the estimators.
The standard models for statistical signal extraction assume that the signal and noise are
generated by linear Gaussian processes. The optimum filter weights for those models are
derived using the method of minimum mean square error. In the present work we study
the properties of signal extraction models under the assumption that signal/noise are
generated by symmetric stable processes. The optimum filter is obtained by the method of
minimum dispersion. The performance of the new filter is compared with their Gaussian
counterparts by simulation.
Sunoj, S M; Linu, M N(Taylor & Francis, May 2, 2010)
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Recently, cumulative residual entropy (CRE) has been found to be a new measure of information that
parallels Shannon’s entropy (see Rao et al. [Cumulative residual entropy: A new measure of information,
IEEE Trans. Inform. Theory. 50(6) (2004), pp. 1220–1228] and Asadi and Zohrevand [On the dynamic
cumulative residual entropy, J. Stat. Plann. Inference 137 (2007), pp. 1931–1941]). Motivated by this finding,
in this paper, we introduce a generalized measure of it, namely cumulative residual Renyi’s entropy,
and study its properties.We also examine it in relation to some applied problems such as weighted and equilibrium
models. Finally, we extend this measure into the bivariate set-up and prove certain characterizing
relationships to identify different bivariate lifetime models
We propose a novel, simple, efficient and distribution-free re-sampling technique for developing prediction intervals for returns and volatilities following ARCH/GARCH models. In particular, our key idea is to employ a Box–Jenkins linear representation of an ARCH/GARCH equation and then to adapt a sieve bootstrap procedure to the nonlinear GARCH framework. Our simulation studies indicate that the new re-sampling method provides sharp and well calibrated prediction intervals for both returns and volatilities while reducing computational costs by up to 100 times, compared to other available re-sampling techniques for ARCH/GARCH models. The proposed procedure is illustrated by an application to Yen/U.S. dollar daily exchange rate data.
Sunoj, S M; Linu, M N; Navarro, J(Elsevier, June 14, 2011)
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In this paper, the residual Kullback–Leibler discrimination information measure is
extended to conditionally specified models. The extension is used to characterize some
bivariate distributions. These distributions are also characterized in terms of proportional
hazard rate models and weighted distributions. Moreover, we also obtain some bounds
for this dynamic discrimination function by using the likelihood ratio order and some
preceding results.
Description:
Statistics and Probability Letters 81 (2011) 1594–1598
Sunoj, S M; Sankaran, P G(Elsevier, March 3, 2012)
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Quantile functions are efficient and equivalent alternatives to distribution functions
in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran,
2009). Motivated by this, in the present paper, we introduce a quantile based Shannon
entropy function. We also introduce residual entropy function in the quantile setup and
study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the
residual quantile entropy function determines the quantile density function uniquely
through a simple relationship. The measure is used to define two nonparametric classes
of distributions
Description:
Statistics and Probability Letters 82 (2012) 1049–1053