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Statistical learning and predictive ...

Li, Bin.

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Statistical learning and predictive modeling in data mining.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical learning and predictive modeling in data mining./
作者:	Li, Bin.
面頁冊數:	82 p.
附註:	Adviser: Prem K. Goel.
Contained By:	Dissertation Abstracts International67-07B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3226445
ISBN:	9780542782091

Statistical learning and predictive modeling in data mining.
Li, Bin.

Statistical learning and predictive modeling in data mining. - 82 p.

Adviser: Prem K. Goel.

Thesis (Ph.D.)--The Ohio State University, 2006.

This research effort focuses on Bayesian robustness properties of regularized optimization methods and developing a hybrid predictive modeling strategy that emphasizes model interpretation.

ISBN: 9780542782091Subjects--Topical Terms:

517247
Statistics.

Statistical learning and predictive modeling in data mining.
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100 1 $a Li, Bin. $3 834070
245 1 0 $a Statistical learning and predictive modeling in data mining.
300 $a 82 p.
500 $a Adviser: Prem K. Goel.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3878.
502 $a Thesis (Ph.D.)--The Ohio State University, 2006.
520 $a This research effort focuses on Bayesian robustness properties of regularized optimization methods and developing a hybrid predictive modeling strategy that emphasizes model interpretation.
520 $a It is known that many regularized optimization methods have Bayesian interpretation. In the first part of the thesis, we consider a class of flat-tailed priors for a general likelihood function in the same spirit as the ' t-distribution suggested as a flat-tail prior for normal likelihood'. We formalize the robustness property in terms of the relative tail behaviors of the likelihood and the priors. Using this setup, we examine the robustness properties for bridge regression family and group LASSO, as well as the consistency issue for the LASSO solution.
520 $a In the second part, we suggest a two-phase boosting method, called "additive regression tree and smoothing splines" (ARTSS), which is highly competitive in predictive performance. However, unlike many automated learning procedures, which lack interpretability and operate as a "black box", ARTSS allows us to (1) estimate the marginal effect smoothly; (2) test the significance of non-additive effects; (3) provide a measure of relative variable importance on main effects and interactions; (4) select variables and/or incorporating hierarchical structure in modeling. Finally, we apply ARTSS to two large public domain data sets and discuss the understanding developed from the model.
590 $a School code: 0168.
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710 2 $a The Ohio State University. $3 718944
773 0 $t Dissertation Abstracts International $g 67-07B.
790 $a 0168
790 1 0 $a Goel, Prem K., $e advisor
791 $a Ph.D.
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