Random environment binomial thinning integer-valued autoregressive process with Poisson or geometric marginal

2020 ◽  
Vol 34 (2) ◽  
pp. 251-272
Author(s):  
Zhengwei Liu ◽  
Qi Li ◽  
Fukang Zhu
Keyword(s):  
Random Environment ◽  
Autoregressive Process ◽  
Binomial Thinning

Random environment integer-valued autoregressive process

2015 ◽  
Vol 37 (2) ◽  
pp. 267-287 ◽  
Author(s):  
Aleksandar S. Nastić ◽  
Petra N. Laketa ◽  
Miroslav M. Ristić
Keyword(s):  
Random Environment ◽  
Autoregressive Process

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.

Driven periodic structures in random environment

1999 ◽  
Vol 09 (PR10) ◽  
pp. Pr10-85-Pr10-87
Author(s):  
V. M. Vinokur
Keyword(s):  
Random Environment ◽  
Periodic Structures

Driven elastic system in a random environment

1999 ◽  
Vol 09 (PR10) ◽  
pp. Pr10-69-Pr10-71 ◽  
Author(s):  
P. Chauve ◽  
T. Giamarchi ◽  
P. Le Doussal
Keyword(s):  
Random Environment ◽  
Elastic System

On the generation and origin of I/f noise

1999 ◽  
Vol 4 ◽  
pp. 87-96 ◽  
Author(s):  
B. Kaulakys ◽  
T. Meškauskas
Keyword(s):  
Point Process ◽  
Solvable Model ◽  
Autoregressive Process ◽  
Wide Range ◽  
Occurrence Times

Simple analytically solvable model exhibiting 1/f spectrum in any desirably wide range of frequency is analysed. The model consists of pulses (point process) whose interevent times obey an autoregressive process with small damping. Analysis and generalizations of the model indicate to the possible origin of 1/f noise, i.e. random increments between the occurrence times of particles or pulses resulting in the clustering of the pulses.

The Effects of Environmental Autocorrelations on the Progress of Selection in a Random Environment

1978 ◽  
Vol 112 (987) ◽  
pp. 897-909 ◽  
Author(s):  
John H. Gillespie ◽  
Harry A. Guess
Keyword(s):  
Random Environment

scholarly journals A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 62
Author(s):  
Zhengwei Liu ◽  
Fukang Zhu
Keyword(s):  
Likelihood Estimation ◽  
Real Data ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Data Sets ◽  
Binomial Thinning ◽  
Free Case ◽  
Two Parameters ◽  
Conditional Maximum ◽  
Thinning Operator

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.

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