Modelling with the Novel INAR(1)-PTE Process

In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimat...

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Bibliographic Details
Published inMethodology and computing in applied probability Vol. 24; no. 3; pp. 1735 - 1751
Main Authors Altun, Emrah, Khan, Naushad Mamode
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2022
Springer Nature B.V
Subjects
Autoregressive processes
Binomial distribution
Business and Management
Datasets
Economics
Electrical Engineering
Innovations
Life Sciences
Mathematics and Statistics
Parameter estimation
Process parameters
Random variables
Simulation
Statistics
Time series
Poisson-transmuted exponential distribution
Conditional maximum likelihood
Over-dispersion
62E15
Binomial thinning
INAR process
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Summary:In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimate the unknown parameters of the proposed process. Additionally, the simulation study is presented to assess the efficiencies of these estimation methods. Applications to two real-life data sets illustrate the usefulness of the proposed process.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1387-5841
1573-7713
DOI:10.1007/s11009-021-09878-2