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High-dimensional statistical learnin...

Feng, Yang.

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High-dimensional statistical learning and nonparametric modeling.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	High-dimensional statistical learning and nonparametric modeling./
作者:	Feng, Yang.
面頁冊數:	143 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3740.
Contained By:	Dissertation Abstracts International71-06B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3410871
ISBN:	9781124051390

High-dimensional statistical learning and nonparametric modeling.
Feng, Yang.

High-dimensional statistical learning and nonparametric modeling. - 143 p.

Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3740.

Thesis (Ph.D.)--Princeton University, 2010.

High-dimensional data analysis has become increasingly frequent and important in various fields of sciences, engineering, and humanities, ranging from computational biology and health studies to financial engineering and risk management. This thesis addresses two important problems in high-dimensional data: estimation of network structure (precision matrix) among the variables and variable selection in ultrahigh additive models as well as the Cox's proportional hazards model. For network estimation in graphical models, we introduce non-concave penalties such as SCAD and the adaptive LASSO penalty to attenuate the bias problem. Simulation experiments and asymptotic theory are used to justify the proposed methods. In the second part, we extend the correlation learning proposed in Fan and Lv (2008) to marginal nonparametric learning in the ultrahigh sparse additive model as well as the Cox's proportional hazards model. The corresponding iterative versions are also provided to further reduce the false selection rate. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy the sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. In addition, the thesis proposes a unified family of parametrically-guided nonparametric estimation schemes with theoretical justification and numerical demonstration. This combines the merits of both parametric and nonparametric approaches and enables us to incorporate prior knowledge.

ISBN: 9781124051390Subjects--Topical Terms:

1018416
Biology, Biostatistics.

High-dimensional statistical learning and nonparametric modeling.
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