Abstract
This paper presents stopping rules and associated estimators, with prescribed mean squared errors, of the regression parameters in stochastic regression models. The construction makes fundamental use of the martingale structure of least squares estimates or their modifications. For one-dimensional regressors, the stopping rules simply stop as soon as the conditional variance of the underlying martingale exceeds some suitably chosen threshold. We show how this idea can be modified for the case of multidimensional stochastic regressors.
Original language | English |
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Pages (from-to) | 179-192 |
Number of pages | 14 |
Journal | Sequential Analysis |
Volume | 14 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Jan 1995 |
Keywords
- autoregression
- fixed precision estimators
- martingales
- sequential estimation
- stochastic regressors
ASJC Scopus subject areas
- Modelling and Simulation
- Statistics and Probability