Estimation in linear models using gradient descent with early stopping |
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Authors: | K Skouras C Goutis M J Bramson |
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Institution: | (1) Department of Statistical Science, University College, London, Gower Street, WC1E 6BT London, UK;(2) Clarendon Consultants, 101 Clarendon Gardens, HA9 7LF Wembley, Middlesex, UK |
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Abstract: | A new shrinkage estimator of the coefficients of a linear model is derived. The estimator is motivated by the gradient-descent algorithm used to minimize the sum of squared errors and results from early stopping of the algorithm. The statistical properties of the estimator are examined and compared with other well-established methods such as least squares and ridge regression, both analytically and through a simulation study. An important result is that the new estimator is shown to be comparable to other shrinkage estimators in terms of mean squared error of parameters and of predictions, and superior under certain circumstances.Supported by the Greek State Scholarships Foundation |
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Keywords: | Biased estimation mean squared error neural networks ridge regression shrinkage estimators |
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