A negative binomial thinning‐based bivariate INAR(1) process

2020 ◽  
Vol 74 (4) ◽  
pp. 517-537
Author(s):  
Qingchun Zhang ◽  
Dehui Wang ◽  
Xiaodong Fan
Keyword(s):  
Negative Binomial ◽  
Binomial Thinning

scholarly journals A mixed thinning based geometric INAR(1) model

Filomat ◽  
2017 ◽  
Vol 31 (13) ◽  
pp. 4009-4022 ◽  
Author(s):  
Aleksandar Nastic ◽  
Miroslav Ristic ◽  
Ana Janjic
Keyword(s):  
Parameter Estimation ◽  
Method Of Moments ◽  
Negative Binomial ◽  
Real Life ◽  
Binomial Thinning ◽  
Conditional Least Squares ◽  
Counting Data ◽  
Parameter Estimators ◽  
Thinning Operator

In this article a geometrically distributed integer-valued autoregressive model of order one based on the mixed thinning operator is introduced. This new thinning operator is defined as a probability mixture of two well known thinning operators, binomial and negative binomial thinning. Some model properties are discussed. Method of moments and the conditional least squares are considered as possible approaches in model parameter estimation. Asymptotic characterization of the obtained parameter estimators is presented. The adequacy of the introduced model is verified by its application on a certain kind of real-life counting data, while its performance is evaluated by comparison with two other INAR(1) models that can be also used over the observed data.

scholarly journals Estimation for random coefficient integer-valued autoregressive model under random environment

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yan Cui ◽  
Yun Y. Wang
Keyword(s):  
Random Environment ◽  
Autoregressive Model ◽  
Strong Consistency ◽  
Negative Binomial ◽  
Random Coefficient ◽  
Model Parameters ◽  
Binomial Thinning ◽  
First Order ◽  
Proposed Model ◽  
Thinning Operator

AbstractA first-order random coefficient integer-valued autoregressive model based on the negative binomial thinning operator under r states random environment is introduced. This paper derives numerical characteristics of the proposed model, establishes Yule–Walker estimators of model parameters, and discusses the strong consistency of the obtained estimators. Finally, a simulation is carried out to verify the feasibility of parameter estimation.

A seasonal geometric INAR process based on negative binomial thinning operator

2018 ◽  
Vol 61 (6) ◽  
pp. 2561-2581 ◽  
Author(s):  
Shengqi Tian ◽  
Dehui Wang ◽  
Shuai Cui
Keyword(s):  
Negative Binomial ◽  
Binomial Thinning ◽  
Thinning Operator

scholarly journals A MIXED BILINEAR INAR(1) MODEL

2021 ◽  
pp. 143
Author(s):  
Predrag M. Popović
Keyword(s):  
Method Of Moments ◽  
Maximum Likelihood Method ◽  
Negative Binomial ◽  
Real Data ◽  
Random Coefficients ◽  
Likelihood Method ◽  
Data Set ◽  
Binomial Thinning ◽  
Conditional Maximum ◽  
Thinning Operator

The paper introduces a new autoregressive model of order one for time seriesof counts. The model is comprised of a linear as well as bilinear autoregressive component. These two components are governed by random coefficients. The autoregression is achieved by using the negative binomial thinning operator. The method of moments and the conditional maximum likelihood method are discussed for the parameter estimation. The practicality of the model is presented on a real data set.

scholarly journals A combined geometric INAR(p) model based on negative binomial thinning

2012 ◽  
Vol 55 (5-6) ◽  
pp. 1665-1672 ◽  
Author(s):  
Aleksandar S. Nastić ◽  
Miroslav M. Ristić ◽  
Hassan S. Bakouch
Keyword(s):  
Negative Binomial ◽  
Binomial Thinning ◽  
Model Based

scholarly journals An INAR(1) Model Based on Negative Binomial Thinning Operator with Serially Dependent Noise.

2020 ◽  
Vol 14 (1) ◽  
pp. 217-234
Author(s):  
Mehrnaz Mohammadpour ◽  
Masoumeh Shirozhan ◽  
◽  
Keyword(s):  
Negative Binomial ◽  
Binomial Thinning ◽  
Model Based ◽  
Thinning Operator

scholarly journals Correction to: A seasonal geometric INAR process based on negative binomial thinning operator

2019 ◽  
Vol 60 (4) ◽  
pp. 1421-1421
Author(s):  
Shengqi Tian ◽  
Dehui Wang ◽  
Shuai Cui
Keyword(s):  
Negative Binomial ◽  
Binomial Thinning ◽  
Thinning Operator
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