Title
Geometrijski minifikacioni vremenski nizovi generisani modifikovanim negativnim binomnim operatorom
Creator
Stojanović, Milena, 1993-
CONOR:
119246857
Copyright date
2024
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
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Language
Serbian
Cobiss-ID
Theses Type
Doktorska disertacija
description
Datum odbrane: 23.09.2024.
Other responsibilities
Academic Expertise
Prirodno-matematičke nauke
Academic Title
-
University
Univerzitet u Nišu
Faculty
Prirodno-matematički fakultet
Group
Odsek za matematiku i informatiku
Alternative title
Geometric minification time series models generated by the modified negative binomial operator
Publisher
[M. S. Stojanović]
Format
[6], 111 listova
description
Biografija: listovi 110-111
Bibliografija: list [112]
description
Time series analysis
Abstract (en)
This thesis introduces new minification INAR(1) models. One-dimensional and two-dimensional models are introduced. The models are based upon the modified negative binomial operator. The most important statistical properties of the models are determined. Model parameter estimation is performed using various methods. All the methods are tested on a simulated dataset. An application to a real dataset is presented. Special attention is devoted to the analytical determination of parameter estimates for the one-dimensional minification INAR(1) model, via the application of the maximum likelihood method. An EM algorithm is constructed. The quality of estimates and the speed of the algorithm are tested on a simulated dataset.
Authors Key words
minifikacioni modeli, celobrojni autoregresivni modeli, modifikovani negativni binomni operator, geometrijska marginalna raspodela, EM algoritam
Authors Key words
minification models, integer-valued autoregressive models, modified negative binomial operator, geometric marginal distribution, EM algorithm
Classification
519.246.8(043.3)
Subject
P160
Type
Tekst
Abstract (en)
This thesis introduces new minification INAR(1) models. One-dimensional and two-dimensional models are introduced. The models are based upon the modified negative binomial operator. The most important statistical properties of the models are determined. Model parameter estimation is performed using various methods. All the methods are tested on a simulated dataset. An application to a real dataset is presented. Special attention is devoted to the analytical determination of parameter estimates for the one-dimensional minification INAR(1) model, via the application of the maximum likelihood method. An EM algorithm is constructed. The quality of estimates and the speed of the algorithm are tested on a simulated dataset.
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