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On the Statistical Dependence - From...

Li, Yi.

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On the Statistical Dependence - From Nonlinearity to Spatial Structural Estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	On the Statistical Dependence - From Nonlinearity to Spatial Structural Estimation./
Author:	Li, Yi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	157 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10837836
ISBN:	9780438140424

On the Statistical Dependence - From Nonlinearity to Spatial Structural Estimation.
Li, Yi.

On the Statistical Dependence - From Nonlinearity to Spatial Structural Estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 157 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--Northeastern University, 2018.

In this thesis, we discuss several dependence related problems in statistics, which covers three topics I worked on during my PhD study. In the first part, we introduce a new concept of robust-equitability for (nonlinear) dependence measures and identify a robust-equitable copula dependence measure (RCD). We also apply RCD in the application of feature ranking and selection. In the second part, we focus on the estimation of high dimensional spatial covariance matrix. Under the block bandable assumption which arises naturally from spatially correlated data, we propose a rate optimal double tapering estimator for estimating the spatial covariance matrix and demonstrate its advantages over the sample covariance matrix. The third part generalizes the former one in the way that it considers the dependence from both nearby and remote distance grid points, and propose a far-near covariance model which could potentially be used to model teleconnection effect in climate research.

ISBN: 9780438140424Subjects--Topical Terms:

517247
Statistics.

On the Statistical Dependence - From Nonlinearity to Spatial Structural Estimation.
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500 $a Adviser: Aidong Ding.
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520 $a In this thesis, we discuss several dependence related problems in statistics, which covers three topics I worked on during my PhD study. In the first part, we introduce a new concept of robust-equitability for (nonlinear) dependence measures and identify a robust-equitable copula dependence measure (RCD). We also apply RCD in the application of feature ranking and selection. In the second part, we focus on the estimation of high dimensional spatial covariance matrix. Under the block bandable assumption which arises naturally from spatially correlated data, we propose a rate optimal double tapering estimator for estimating the spatial covariance matrix and demonstrate its advantages over the sample covariance matrix. The third part generalizes the former one in the way that it considers the dependence from both nearby and remote distance grid points, and propose a far-near covariance model which could potentially be used to model teleconnection effect in climate research.
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