Certain inference problems for multivariate hypergeometric models

Janardan, K. G.

doi:10.1080/03610927508827254

Cited by 14 publications

(18 citation statements)

References 8 publications

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“…More interesting examples of LPSDs have been studied in depth by Janardan and Patil [5]. Chance mechanisms for various multivariate hypergeometric-type distributions are discussed in [4].…”

Section: Lauricella Power Series Distributions (Lpsds)mentioning

confidence: 98%

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New discrete Appell and Humbert distributions with relevance to bivariate accident data

Kemp

2013

Journal of Multivariate Analysis

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a b s t r a c tThe paper categorizes discrete multivariate distributions into classes according to the forms of their probability generating functions, putting especial emphasis on those with pgf's involving Lauricella functions. The LPSDs are Lauricella power series distributions where the arguments of the function are proportional to the generating variables. Lauricella factorial moment distributions, LFMDs, have arguments of the form λ i (s i − 1), where s i is a generating variable. New LFMDs are created; the differences between these and Xekalaki's generalized Waring distribution are clarified using bivariate accident models.

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“…More interesting examples of LPSDs have been studied in depth by Janardan and Patil [5]. Chance mechanisms for various multivariate hypergeometric-type distributions are discussed in [4].…”

Section: Lauricella Power Series Distributions (Lpsds)mentioning

confidence: 98%

“…[19,20], were mainly concerned with the derivation of their properties via their probability mass functions. Janardan and Patil [5] advanced the subject considerably by introducing a unified multivariate hypergeometric distribution with k variables and pgf…”

Section: Lauricella Power Series Distributions (Lpsds)mentioning

confidence: 99%

New discrete Appell and Humbert distributions with relevance to bivariate accident data

Kemp

2013

Journal of Multivariate Analysis

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“…= (−1) θ −1 /(θ − 1)! for a positive integer θ , Janardan and Patil [35] defined multivariate inverse hypergeometric, multivariate negative hypergeometric, and multivariate negative inverse hypergeometric distributions. Sibuya and Shimizu [83] studied the multivariate generalized hypergeometric distributions with probability mass function…”

Section: Related Distributionsmentioning

confidence: 99%

“…Janardan and Patil [35] termed a subclass of Steyn's [89] family as unified multivariate hypergeometric distributions and its probability mass function is…”

Section: Related Distributionsmentioning

confidence: 99%

Discrete Multivariate Distributions

Balakrishnan

2005

Encyclopedia of Statistical Sciences

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“…Hartley and Rao [4] are mainly concerned with finding unbiased minimum variance and ML estimates of certain functions of the population parameters but they do also have a section on Bayes' approach with MNH as a prior distribution. Janardan and Patil [8] have shown that multivariate hypergeometric (MH), multivariate inverse hypergeometric (MIH), multivariate negative hypergeometric (MNIH), multivariate Polya (MP) and multivariate inverse Polya (MIP) distributions belong to a class of multivariate distributions called unified multivariate hypergeometric (UMH) class. The probability functions of these models are given in Table 1. Mosimann [9], [10] has discussed the moment estimates of the parameters of MNH and MNIH.…”

Section: Introductionmentioning

confidence: 99%

Certain estimation problems for multivariate hypergeometric models

Janardan

1976

Ann Inst Stat Math

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Certain estimation problems associated with the multivariate hypergeometric models: the property of completeness, maximum likelihood estimates of the parameters of multivariate negative hypergeometric, multivariate negative inverse hypergeometric, Bayesian estimation of the parameters of multivariate hypergeometric and multivariate inverse hypergeometrics are discussed in this paper.A two stage approach for generating the prior distribution, first by setting up a parametric super population and then choosing a prior distribution is followed. Posterior expectations and variances of certain functions of the parameters of the finite population are provided in cases of direct and inverse sampling procedures. It is shown that under extreme diffuseness of prior knowledge the posterior distribution of the finite population mean has an approximate mean ~ and variance (N-n)S2/Nn, providing a Bayesian interpretation for the classical unbiased estimates in traditional sample survey theory.

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Certain inference problems for multivariate hypergeometric models

Cited by 14 publications

References 8 publications

New discrete Appell and Humbert distributions with relevance to bivariate accident data

New discrete Appell and Humbert distributions with relevance to bivariate accident data

Discrete Multivariate Distributions

Certain estimation problems for multivariate hypergeometric models

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