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Cited by 13 publications
(5 citation statements)
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“…We select three competitive bivariate models to fit these overdispersed counts, they are as follows: The full BINAR(1) process with bivariate negative binomial innovations (full BINAR(1)-BNB, Pedeli and Karlis 8 ). The full BINAR(1) process with bivariate Poisson innovations (full BINAR(1)-BP, Pedeli and Karlis 8 ). The full BINAR(1) process with bivariate Poisson–Lindley innovations (full BINAR(1)-BPL, Khan et al. 15 ). The maximum likelihood estimation results of these models are shown in Table 1, with the corresponding log-likelihood function values, Akaike information criterion (AIC), and root mean square (RMS) value. The RMS denotes the root mean squares of differences between observed values and one-step predicted values (Aleksandrov and Weiß 29 ).…”
Section: Application and Performance Of Control Chartsmentioning
confidence: 99%
See 2 more Smart Citations
“…We select three competitive bivariate models to fit these overdispersed counts, they are as follows: The full BINAR(1) process with bivariate negative binomial innovations (full BINAR(1)-BNB, Pedeli and Karlis 8 ). The full BINAR(1) process with bivariate Poisson innovations (full BINAR(1)-BP, Pedeli and Karlis 8 ). The full BINAR(1) process with bivariate Poisson–Lindley innovations (full BINAR(1)-BPL, Khan et al. 15 ). The maximum likelihood estimation results of these models are shown in Table 1, with the corresponding log-likelihood function values, Akaike information criterion (AIC), and root mean square (RMS) value. The RMS denotes the root mean squares of differences between observed values and one-step predicted values (Aleksandrov and Weiß 29 ).…”
Section: Application and Performance Of Control Chartsmentioning
confidence: 99%
“…The full BINAR(1) process with bivariate Poisson–Lindley innovations (full BINAR(1)-BPL, Khan et al. 15 ).…”
Section: Application and Performance Of Control Chartsmentioning
confidence: 99%
See 1 more Smart Citation
“…In addition, Ref. [30] used a bivariate Poisson-Lindley as an innovation distribution for a BINAR model. Finally, actuarial usage of such a family is used in [31,32].…”
Section: Sarmanov Familymentioning
confidence: 99%
“…As for the crime data, can be considered as the number of re-offendings provoked by with probability . Depending on the nature of this kind of observed data, the INAR(1) models have been modified and generalized with respect to their orders (Ristić and Nastić [ 3 ], Nastić, Laketa and Ristić [ 4 ]), dimensions (Pedeli and Karlis [ 5 ], Khan, Cekim and Ozel [ 6 ]), marginal distributions (Alzaid and Al-Osh [ 7 ], Alzaid and Al-Osh [ 8 ], Jazi, Jones and Lai [ 9 ], Ristić, Nastić and Bakouch [ 10 ], Barreto-Souza [ 11 ]), thinning operators (Ristić, Bakouch and Nastić[ 12 ], Liu and Zhu [ 13 ]), and mixed models (Ristić and Nastić [ 3 ], Li, Wang and Zhang [ 14 ], Orozco, Sales, Fernández and Pinho [ 15 ]). For more literature, we refer to review papers (Weiß [ 16 ], Scotto, Weiß and Gouveia [ 17 ]).…”
Section: Introductionmentioning
confidence: 99%