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Nonparametric prediction intervals.

Zhou, Lan.

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Nonparametric prediction intervals.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonparametric prediction intervals./
Author:	Zhou, Lan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 1997,
Description:	100 p.
Notes:	Source: Dissertations Abstracts International, Volume: 60-01, Section: B.
Contained By:	Dissertations Abstracts International60-01B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9827173
ISBN:	9780591795233

Nonparametric prediction intervals.
Zhou, Lan.

Nonparametric prediction intervals. - Ann Arbor : ProQuest Dissertations & Theses, 1997 - 100 p.

Source: Dissertations Abstracts International, Volume: 60-01, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 1997.

This item must not be sold to any third party vendors.

Prediction intervals are useful statistical tools in understanding the uncertainty of future happenings. Nonparametric prediction intervals provide inferences without requiring specific parametric assumptions about the sampling distribution. As the unified theme of this thesis, we pursue a theoretical investigation of nonparametric prediction intervals. Two major construction principles, controlling overall coverage probability and controlling conditional coverage probability given the learning sample, are reviewed. The relationship between the nonparametric prediction intervals and the nonparametric confidence intervals is studied in Chapter 2. Chapter 3 compares the two construction principles and various methods for i. i. d. observations. We give the asymptotic properties of the constructed prediction intervals and in addition, we obtain a local asymptotic minimax bound for conditional coverage probabilities. Chapter 4 focuses on the nonparametric prediction intervals in stationary time series. We propose a way of constructing the prediction interval by controlling the conditional coverage probability given the observed sample and show that such an interval is asymptotically correct. The limiting distribution of the conditional coverage probability is also obtained in the context of stationary autoregressive models.

ISBN: 9780591795233Subjects--Topical Terms:

517247
Statistics.

Nonparametric prediction intervals.
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020 $a 9780591795233
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100 1 $a Zhou, Lan. $3 3174283
245 1 0 $a Nonparametric prediction intervals.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 1997
300 $a 100 p.
500 $a Source: Dissertations Abstracts International, Volume: 60-01, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Beran, Rudolph.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 1997.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Prediction intervals are useful statistical tools in understanding the uncertainty of future happenings. Nonparametric prediction intervals provide inferences without requiring specific parametric assumptions about the sampling distribution. As the unified theme of this thesis, we pursue a theoretical investigation of nonparametric prediction intervals. Two major construction principles, controlling overall coverage probability and controlling conditional coverage probability given the learning sample, are reviewed. The relationship between the nonparametric prediction intervals and the nonparametric confidence intervals is studied in Chapter 2. Chapter 3 compares the two construction principles and various methods for i. i. d. observations. We give the asymptotic properties of the constructed prediction intervals and in addition, we obtain a local asymptotic minimax bound for conditional coverage probabilities. Chapter 4 focuses on the nonparametric prediction intervals in stationary time series. We propose a way of constructing the prediction interval by controlling the conditional coverage probability given the observed sample and show that such an interval is asymptotically correct. The limiting distribution of the conditional coverage probability is also obtained in the context of stationary autoregressive models.
590 $a School code: 0028.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a University of California, Berkeley. $3 687832
773 0 $t Dissertations Abstracts International $g 60-01B.
790 $a 0028
791 $a Ph.D.
792 $a 1997
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9827173