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An Information Based Optimal Subdata...

Zheng, Yi.

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An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.

Record Type:	Electronic resources : Monograph/item
Title/Author:	An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm./
Author:	Zheng, Yi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	46 p.
Notes:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275712
ISBN:	9781369767995

An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.
Zheng, Yi.

An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 46 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.S.)--Arizona State University, 2017.

This article proposes a new information-based subdata selection (IBOSS) algorithm, Squared Scaled Distance Algorithm (SSDA). It is based on the invariance of the determinant of the information matrix under orthogonal transformations, especially rotations. Extensive simulation results show that the new IBOSS algorithm retains nice asymptotic properties of IBOSS and gives a larger determinant of the subdata information matrix. It has the same order of time complexity as the D-optimal IBOSS algorithm. However, it exploits the advantages of vectorized calculation avoiding for loops and is approximately 6 times as fast as the D-optimal IBOSS algorithm in R. The robustness of SSDA is studied from three aspects: nonorthogonality, including interaction terms and variable misspecification. A new accurate variable selection algorithm is proposed to help the implementation of IBOSS algorithms when a large number of variables are present with sparse important variables among them. Aggregating random subsample results, this variable selection algorithm is much more accurate than the LASSO method using full data. Since the time complexity is associated with the number of variables only, it is also very computationally efficient if the number of variables is fixed as n increases and not massively large. More importantly, using subsamples it solves the problem that full data cannot be stored in the memory when a data set is too large.

ISBN: 9781369767995Subjects--Topical Terms:

517247
Statistics.

An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.
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100 1 $a Zheng, Yi. $3 1034006
245 1 3 $a An Information Based Optimal Subdata Selection Algorithm for Big Data Linear Regression and a Suitable Variable Selection Algorithm.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 46 p.
500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: John Stufken.
502 $a Thesis (M.S.)--Arizona State University, 2017.
520 $a This article proposes a new information-based subdata selection (IBOSS) algorithm, Squared Scaled Distance Algorithm (SSDA). It is based on the invariance of the determinant of the information matrix under orthogonal transformations, especially rotations. Extensive simulation results show that the new IBOSS algorithm retains nice asymptotic properties of IBOSS and gives a larger determinant of the subdata information matrix. It has the same order of time complexity as the D-optimal IBOSS algorithm. However, it exploits the advantages of vectorized calculation avoiding for loops and is approximately 6 times as fast as the D-optimal IBOSS algorithm in R. The robustness of SSDA is studied from three aspects: nonorthogonality, including interaction terms and variable misspecification. A new accurate variable selection algorithm is proposed to help the implementation of IBOSS algorithms when a large number of variables are present with sparse important variables among them. Aggregating random subsample results, this variable selection algorithm is much more accurate than the LASSO method using full data. Since the time complexity is associated with the number of variables only, it is also very computationally efficient if the number of variables is fixed as n increases and not massively large. More importantly, using subsamples it solves the problem that full data cannot be stored in the memory when a data set is too large.
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650 4 $a Computer science. $3 523869
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10275712