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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/islightboxThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/69724 69766 2 2410027 document 69766 preview.jpg image/png 08292c2805598aa7bf25ac0b735e165f MD5 29135 2020-10-16 01:37:18 https://vuir.vu.edu.au/1735/3/preview.jpg 1735 3 3 image/png other Thumbnails conversion from text to thumbnail_preview en staffonly

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/ismediumThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/69724 69768 2 2410029 document 69768 small.jpg image/png 847db1003f1aef8686830fd2ccc9e7c2 MD5 1750 2020-10-16 01:37:19 https://vuir.vu.edu.au/1735/5/small.jpg 1735 5 5 image/png other Thumbnails conversion from text to thumbnail_small en staffonly

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/69724 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/69724 archive 293 disk0/00/00/17/35 2009-02-15 14:06:20 2021-08-27 07:42:04 2009-02-15 14:06:20 article show Huang Fuchun Huang, Fuchun pub 290000 admissibility, generalized lasso estimator, lasso-shrinkable condition, least squares estimator The lasso procedure is an estimator-shrinkage and variable selection method. This paper shows that there always exists and interval of tuning parameter values such that the cor-responding mean spuared prediction error for the lasso estimator is smaller than for the ordinary least spuares estimator. For an estimator satisfying some condition such as unbi-asedness, the paper defines the corresponding generalized lasso estimator. Its mean squared prediction error is shown to be smaller than that of the estimator for values of the tuning parameter in some interval. This implies that all unbiased estimators are not admissible. Simulation results for five models support the theoretical results. 2003-04-22 published Wiley-Blackwell Publishing Asia 10.1111/1467-842X.00277 http://dx.doi.org/10.1111/1467-842X.00277 69724 2 2409968 dark_document 69724 Huang Fuchun.pdf application/pdf e291d1aa0a868cff2cbce8e484ef15e5 MD5 72899 2020-10-16 01:27:15 1735 1 1 application/pdf text Generic Permission Form en staffonly

Huang Fuchun.pdf

other none Australian & New Zealand Journal of Statistics 45 2 217-228 TRUE 1369-1473 FALSE 6 2-s2.0-0038583952 2023-07-02 04:01:05