Geodesic
Prediction problems related to a first-order autoregressive process in the presence of outliers
Sugata Sen Roy1 ; Sourav Chakraborty2
1 Department of Statistics University of Calcutta 35, Ballygunge Circular Road Calcutta 700019, India
2 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
Applicationes Mathematicae, Tome 33 (2006) no. 3-4, pp. 265-274 Cet article a éte moissonné depuis la source Institute of Mathematics Polish Academy of Sciences

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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.
DOI : 10.4064/am33-3-2
Keywords: outliers time series often cause problems fitting suitable model hence predictions based models liable erroneous paper consider stable first order autoregressive process suggest methods substituting outlier imputed values predicting basis asymptotic properties process parameter estimators predictors studied

Sugata Sen Roy 1 ; Sourav Chakraborty 2

1 Department of Statistics University of Calcutta 35, Ballygunge Circular Road Calcutta 700019, India
2 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 
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 in the presence of outliers
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Sugata Sen Roy; Sourav Chakraborty. Prediction problems related to a first-order
 autoregressive process
 in the presence of outliers. Applicationes Mathematicae, Tome 33 (2006) no. 3-4, pp. 265-274. doi: 10.4064/am33-3-2

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