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Sunil Kumar Gauttam

Publications and source records attributed to Sunil Kumar Gauttam.

4 recordsLinked to original sources
arXiv2026

Parameter Estimation of Incomplete Gamma Subordinators

In this paper, we estimate the parameters of InG, InG-$ε$ and TInG subordinators which have been studied by Babulal \textit{et al} (see \cite{babulal}). We have modified the method of moments technique to use fractional moments of the InG and InG-$ε$ subordinator due to their infinite moments. For the TInG subordinator's parameter estimation, we have used the method of moments. We also compute the maximum likelihood estimator(MLE) for the parameter $α$ of the InG and InG-$ε$ subordinators using jump distribution of the process. We also discussed the asymptotic normality of MLE.

math.ST↗
arXiv2024

Lévy processes with jumps governed by lower incomplete gamma subordinator and its variations

In this paper, we study the Lévy process time-changed by independent Lévy subordinators, namely, the incomplete gamma subordinator, the $ε$-jumps incomplete gamma subordinator and tempered incomplete gamma subordinator. We derive their important distributional properties such as mean, variance, correlation, tail probabilities and fractional moments. The long-range dependence property of these processes are discussed. An application in insurance domain is studied in detail. Finally, we present the simulated sample paths for the subordinators.

math.PR↗
arXiv2024

Parameter estimation and long-range dependence of the fractional binomial process

In 1990, Jakeman (see \cite{jakeman1990statistics}) defined the binomial process as a special case of the classical birth-death process, where the probability of birth is proportional to the difference between a fixed number and the number of individuals present. Later, a fractional generalization of the binomial process was studied by Cahoy and Polito (2012) (see \cite{cahoy2012fractional}) and called it as fractional binomial process (FBP). In this paper, we study second-order properties of the FBP and the long-range behavior of the FBP and its noise process. We also estimate the parameters of the FBP using the method of moments procedure. Finally, we present the simulated sample paths and its algorithm for the FBP.

math.ST↗