Prediction problems related to a first-order autoregressive process in the presence of outliers

• Autorzy: Sugata Sen Roy , Sourav Chakraborty
• Języki publikacji: EN

Abstrakty

EN

Outliers in a time series often cause problems in fitting a suitable model to the data. Hence predictions based on such models are liable to be erroneous. In this paper we consider a stable first-order autoregressive process and suggest two methods of substituting an outlier by imputed values and then predicting on the basis of it. The asymptotic properties of both the process parameter estimators and the predictors are also studied.

Kategorie tematyczne

• 62M10: Time series, auto-correlation, regression, etc.
• 62M20: Prediction

• Wydawca: Institute of Mathematics Polish Academy of Sciences
• Czasopismo: Applicationes Mathematicae
• Rocznik: 2006
• Tom: 33
• Numer: 3-4
• Strony: 265-274

Daty

wydano

2006

Twórcy

autor

Sugata Sen Roy

• Department of Statistics, University of Calcutta, 35, Ballygunge Circular Road, Calcutta 700019, India

autor

Sourav Chakraborty

• Social Statistics Division, Central Statistical Organisation, Ministry of Statistics & Programme Implementation, Government of India, West Block 8, Wing 6, Sector 1, R.K. Puram, New Delhi 110066, India

Identyfikatory

DOI

10.4064/am33-3-2