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Computation and Smoothing Parameter Selection In Penalized Likelihood Regression
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 Title & Authors
Computation and Smoothing Parameter Selection In Penalized Likelihood Regression
Kim Young-Ju;
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 Abstract
This paper consider penalized likelihood regression with data from exponential family. The fast computation method applied to Gaussian data(Kim and Gu, 2004) is extended to non Gaussian data through asymptotically efficient low dimensional approximations and corresponding algorithm is proposed. Also smoothing parameter selection is explored for various exponential families, which extends the existing cross validation method of Xiang and Wahba evaluated only with Bernoulli data.
 Keywords
Cross-validation;Kullback-Leibler;Penalized likelihood;Smoothing parameter;
 Language
Korean
 Cited by
1.
Bayesian Confidence Intervals in Penalized Likelihood Regression,;

Communications for Statistical Applications and Methods, 2006. vol.13. 1, pp.141-150 crossref(new window)
2.
Penalized Likelihood Regression with Negative Binomial Data with Unknown Shape Parameter,;

Communications for Statistical Applications and Methods, 2007. vol.14. 1, pp.23-32 crossref(new window)
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