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

Tom 33 / 2006

Sugata Sen Roy, Sourav Chakraborty Applicationes Mathematicae 33 (2006), 265-274 MSC: Primary 62M10; Secondary 62M20. DOI: 10.4064/am33-3-2


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.


  • Sugata Sen RoyDepartment of Statistics
    University of Calcutta
    35, Ballygunge Circular Road
    Calcutta 700019, India
  • Sourav ChakrabortySocial 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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