arXiv subjects

Subrata Chakraborty

Publications and source records attributed to Subrata Chakraborty.

49 records · Page 3Linked to original sources

arXiv2016

Analysis of Count Data by Transmuted Geometric Distribution

Transmuted geometric distribution (TGD) was recently introduced and investigated by Chakraborty and Bhati (2016). This is a flexible extension of geometric distribution having an additional parameter that determines its zero inflation as well as the tail length. In the present article we further study this distribution for some of its reliability, stochastic ordering and parameter estimation properties. In parameter estimation among others we discuss an EM algorithm and the performance of estimators is evaluated through extensive simulation. For assessing the statistical significance of additional parameter, Likelihood ratio test, the Rao's score tests and the Wald's test are developed and its empirical power via simulation were compared. We have demonstrate two applications of (TGD) in modeling real life count data.

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arXiv2016

A modified Conway-Maxwell-Poisson type binomial distribution and its applications

This paper proposes a generalized binomial distribution with four parameters, which is derived from the finite capacity queueing system with state-dependent service and arrival rates. This distribution is also generated from the conditional Conway-Maxwell-Poisson distribution given a sum of two Conway-Maxwell-Poisson variables. In this paper, we consider the properties about the probability mass function, index of dispersion, skewness and kurtosis and give applications of the proposed distribution from its geneses. The estimation method and simulation study are also considered.

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arXiv2016

The Marshall-Olkin-Kumarswamy-G family of distributions

A new family of continuous distribution is proposed by using Kumaraswamy-G (Cordeiro and de Castro, 2011) distribution as the base line distribution in the Marshal-Olkin (Marshall and Olkin, 1997) construction. A number of known distributions are derived as particular cases. Various properties of the proposed family like formulation of the pdf as different mixture of exponentiated baseline distributions, order statistics, moments, moment generating function, Renyi entropy, quantile function and random sample generation have been investigated. Asymptotes, shapes and stochastic ordering are also investigated. The parameter estimation by methods of maximum likelihood, their large sample standard errors and confidence intervals and method of moment are also presented. Two members of the proposed family are compared with corresponding members of Kumaraswamy-Marshal-Olkin-G family (Alizadeh et al., 2015) by fitting of two real life data sets.

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arXiv2016

The Generalized Marshall-Olkin-Kumaraswamy-G family of distributions

A new family of distribution is proposed by using Kumaraswamy-G (Cordeiro and de Castro, 2011) distribution as the base line distribution in the Generalized Marshal-Olkin (Jayakumar and Mathew, 2008) Construction. A number of special cases are presented. By expanding the probability density function and the survival function as infinite series the proposed family is seen as infinite mixtures of the Kumaraswamy-G (Cordeiro and de Castro, 2011) distribution. Density function and its series expansions for order statistics are also obtained. Order statistics, moments, moment generating function, Rényi entropy, quantile function, random sample generation, asymptotes, shapes and stochastic orderings are also investigated. The methods of parameter estimation by method of maximum likelihood and method of moment are presented. Large sample standard error and confidence intervals for the mles are also discussed. One real life application of comparative data fitting with some of the important sub models of the family and some other models is considered.

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arXiv2016

Beta generated Kumaraswamy-G and other new families of distributions

A new generalization of the family of Kumaraswamy-G (Cordeiro and de Castro, 2011) distribution that includes three recently proposed families namely the Garhy generated family (Elgarhy et al., 2016), Beta-Dagum and Beta-Singh-Maddala distribution (Domma and Condino, 2016) is proposed by constructing beta generated Kumaraswamy-G distribution. Useful expansions of the pdf and the cdf of the proposed family is derived and seen as infinite mixtures of the Kumaraswamy-G distribution. Order statistics, Probability weighted moments, moment generating function, Rényi entropies, quantile power series, random sample generation, asymptotes and shapes are also investigated. Two methods of parameter estimation are presented. Suitability of the proposed model in comparisons to its sub models is carried out considering two real life data sets. Finally, some new classes of beta generated families are proposed for future investigations.

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arXiv2016

The Beta Generalized Marshall-Olkin-G Family of Distributions

In this paper we propose a new family of distribution considering Generalized Marshal-Olkin distribution as the base line distribution in the Beta-G family of Construction. The new family includes Beta-G (Eugene et al. 2002 and Jones, 2004) and (Jayakumar and Mathew, 2008) families as particular cases. Probability density function (pdf) and the cumulative distribution function (cdf) are expressed as mixture of the Marshal-Olkin (Marshal and Olkin, 1997) distribution. Series expansions of pdf of the order statistics are also obtained. Moments, moment generating function, Rényi entropies, quantile power series, random sample generation and asymptotes are also investigated. Parameter estimation by method of maximum likelihood and method of moment are also presented. Finally proposed model is compared to the Generalized Marshall-Olkin Kumaraswamy extended family (Handique and Chakraborty, 2015) by considering three data fitting examples with real life data sets.

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arXiv2016

The Kumaraswamy Generalized Marshall-Olkin-G family of distributions

Another new family of continuous probability distribution is proposed by using Generalized Marshal-Olkin distribution as the base line distribution in the Kumaraswamy-G distribution. This family includes (Cordeiro and de Castro, 2011) and (Jayakumar and Mathew, 2008) families special case besides a under of other distributions. The probability density function (pdf) and the survival function (sf) are expressed as series to observe as a mixture of the Generalized Marshal-Olkin distribution. Series expansions pdf of order statistics are also obtained. Moments, moment generating function, Rényi entropies, quantile function, random sample generation and asymptotes are also investigated. Parameter estimation by method of maximum likelihood and method of moment are also presented. Finally the proposed model is compared to the Generalized Marshall-Olkin Kumaraswamy extended family (Handique and Chakraborty, 2015) by considering four examples of real life data modeling.

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arXiv2016

An Alternative Discrete Skew Logistic Distribution

In this paper, an alternative Discrete skew Logistic distribution is proposed, which is derived by using the general approach of discretizing a continuous distribution while retaining its survival function. The properties of the distribution are explored and it is compared to a discrete distribution defined on integers recently proposed in the literature. The estimation of its parameters are discussed, with particular focus on the maximum likelihood method and the method of proportion, which is particularly suitable for such a discrete model. A Monte Carlo simulation study is carried out to assess the statistical properties of these inferential techniques. Application of the proposed model to a real life data is given as well.

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arXiv2015

Extended Conway-Maxwell-Poisson distribution and its properties and applications

A new three parameter natural extension of the Conway-Maxwell-Poisson (COM-Poisson) distribution is proposed. This distribution includes the recently proposed COM-Poisson type negative binomial (COM-NB) distribution [Chakraborty, S. and Ong, S. H. (2014): A COM-type Generalization of the Negative Binomial Distribution, Accepted in Communications in Statistics-Theory and Methods] and the generalized COM-Poisson (GCOMP) distribution [Imoto, T. :(2014) A generalized Conway-Maxwell-Poisson distribution which includes the negative binomial distribution, Applied Mathematics and Computation, 247, 824-834]. The proposed distribution is derived from a queuing system with state dependent arrival and service rates and also from an exponential combination of negative binomial and COM-Poisson distribution. Some distributional, reliability and stochastic ordering properties are investigated. Computational asymptotic approximations, different characterizations, parameter estimation and data fitting example also discussed.

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arXiv2015

Transmuted Geometric Distribution and its Prpoerties

Transmuted geometric distribution with two parameters and is proposed as a new generalization of the geometric distribution by employing the quadratic transmutation techniques of Shaw and Buckley (2007). Its important distributional and reliability properties are investigated. Parameter estimation methods is discussed.

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arXiv2015

A Discrete Power Distribution

A new discrete distribution has been proposed as a discrete analogue of the two sided power distribution [Van Drop, J. R. and Kotz, S. (2002a). A novel extension of the triangular distribution and its parameter estimation, Journal of the Royal Statistical Society, Series D (The Statistician), 51, 1: 63-79]. This probability mass function and hazard rate function of this distribution can assume a variety of shapes including bath tub, rectangular, trapezoidal, triangular, J, inverse J, U inverse U, strictly decreasing and strictly increasing shapes. Its moment and reliability properties along with parameter estimation have been investigated.

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arXiv2014

Mittag - Leffler function distribution - A new generalization of hyper-Poisson distribution

In this paper a new generalization of the hyper-Poisson distribution is proposed using the Mittag-Leffler function. The hyper-Poisson, displaced Poisson, Poisson and geometric distributions among others are seen as particular cases. This Mittag-Leffler function distribution (MLFD) belongs to the generalized hypergeometric and generalized power series families and also arises as weighted Poison distributions. MLFD is a flexible distribution with varying shapes like non-increasing with unique mode at zero, unimodal with one / two non-zero modes. It can be under, equi or over dispersed. Various distributional properties like recurrence relation for pmf, cumulative distribution function, generating functions, formulae for different type of moments, their recurrence relations, index of dispersion, its classification, log-concavity, reliability properties like survival, increasing failure rate, unimodality, and stochastic ordering with respect to hyper-Poisson distribution have been discussed. The distribution has been found to fare well when compared with the hyper-Poisson distributions in its suitability in empirical modeling of differently dispersed count data. It is therefore expected that proposed MLFD with its interesting features and flexibility, will be a useful addition as a model for count data.

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arXiv2014

A Discrete Gumbel Distribution

A discrete version of the Gumbel (Type I) extreme value distribution has been derived by using the general approach of discretization of a continuous distribution. Important distributional and reliability properties have been explored. It has been shown that depending on the choice of parameters the proposed distribution can be positively or negatively skewed; possess long tail(s), and exhibits equal, over or under dispersion. Log concavity of the distribution and consequential results has been established. Estimation of parameters by method of maximum likelihood, method of moments, and method of proportions has been presented. A method of checking model adequacy and regression type estimation based on empirical survival function has also been examined. Simulation study has been carried out to check the efficacy of the maximum likelihood estimators. Finally, the proposed distribution has been applied to model three real life count data regarding maximum flood discharges and annual maximum wind speeds from literature.

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