Replicated INAR(1) Processes

Replicated time series are a particular type of repeated measures, which consist of time-sequences of measurements taken from several subjects (experimental units). We consider independent replications of count time series that are modelled by first-order integer-valued autoregressive processes, INA...

Full description

Saved in:
Bibliographic Details
Published inMethodology and computing in applied probability Vol. 7; no. 4; pp. 517 - 542
Main Authors Silva, Isabel, Silva, M. Eduarda, Pereira, Isabel, Silva, Nélia
Format Journal Article
LanguageEnglish
Published New York Springer Nature B.V 01.12.2005
Subjects
Autoregressive processes
Bayesian analysis
Computer simulation
Counting
Estimating techniques
Estimators
Frequency domains
Mathematical models
Probability
Replication
Studies
Time series
Online AccessGet full text
ISSN1387-5841
1573-7713
DOI10.1007/s11009-005-5006-x

Cover

More Information
Summary:Replicated time series are a particular type of repeated measures, which consist of time-sequences of measurements taken from several subjects (experimental units). We consider independent replications of count time series that are modelled by first-order integer-valued autoregressive processes, INAR(1). In this work, we propose several estimation methods using the classical and the Bayesian approaches and both in time and frequency domains. Furthermore, we study the asymptotic properties of the estimators. The methods are illustrated and their performance is compared in a simulation study. Finally, the methods are applied to a set of observations concerning sunspot data. [PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ISSN:1387-5841
1573-7713
DOI:10.1007/s11009-005-5006-x