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  • 1
    ISSN: 1573-1375
    Keywords: Biased estimation ; mean squared error ; neural networks ; ridge regression ; shrinkage estimators
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: 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.
    Type of Medium: Electronic Resource
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