Stationary processes are a natural choice as statistical models for time series data, owing to their good estimating properties. In practice, however, alternative models are often proposed that sacrifice stationarity in favour of the greater modelling flexibility required by many real-life applications. We present a family of time-homogeneous Markov processes with nonparametric stationary densities, which retain the desirable statistical properties for inference, while achieving substantial modelling flexibility, matching those achievable with certain non-stationary models. A latent extension of the model enables exact inference through a trans-dimensional Markov chain Monte Carlo method. Numerical illustrations are presented.

A nonparametric model for stationary time series

ANTONIANO VILLALOBOS, ISADORA;
2016

Abstract

Stationary processes are a natural choice as statistical models for time series data, owing to their good estimating properties. In practice, however, alternative models are often proposed that sacrifice stationarity in favour of the greater modelling flexibility required by many real-life applications. We present a family of time-homogeneous Markov processes with nonparametric stationary densities, which retain the desirable statistical properties for inference, while achieving substantial modelling flexibility, matching those achievable with certain non-stationary models. A latent extension of the model enables exact inference through a trans-dimensional Markov chain Monte Carlo method. Numerical illustrations are presented.
2016
2015
ANTONIANO VILLALOBOS, Isadora; Walker, Stephen G.
File in questo prodotto:
File Dimensione Formato  
BNPMTimeSv3.pdf

non disponibili

Descrizione: Articolo principale
Tipologia: Documento in Pre-print (Pre-print document)
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 1.04 MB
Formato Adobe PDF
1.04 MB Adobe PDF   Visualizza/Apri
Acceptance mail.pdf

non disponibili

Descrizione: Email di accettazione
Tipologia: Altro materiale allegato
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 61.06 kB
Formato Adobe PDF
61.06 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/3984967
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 9
social impact