binomial thinning
Recently Published Documents


TOTAL DOCUMENTS

23
(FIVE YEARS 14)

H-INDEX

5
(FIVE YEARS 2)

2021 ◽  
Vol 76 (4) ◽  
Author(s):  
Karol Baron ◽  
Jacek Wesolowski

AbstractWe point out to a connection between a problem of invariance of power series families of probability distributions under binomial thinning and functional equations which generalize both the Cauchy and an additive form of the Gołąb–Schinzel equation. We solve these equations in several settings with no or mild regularity assumptions imposed on unknown functions.


Author(s):  
Predrag M. Popović

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.


Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.


2020 ◽  
Vol 14 (1) ◽  
pp. 217-234
Author(s):  
Mehrnaz Mohammadpour ◽  
Masoumeh Shirozhan ◽  
◽  

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

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Yan Cui ◽  
Yun Y. Wang

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.


Sign in / Sign up

Export Citation Format

Share Document