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Uncertainty and Risk Quantification ...

Jiang, Kuanhao,

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Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings // Kuanhao Jiang.
Author:	Jiang, Kuanhao,
Description:	1 electronic resource (340 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32002503
ISBN:	9798280719040

Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings /
Jiang, Kuanhao,

Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings /Kuanhao Jiang. - 1 electronic resource (340 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

High-dimensional data are increasingly common across fields such as genomics, economics, and neuroscience, often challenging conventional statistical methods. This dissertation develops new tools for uncertainty quantification in high-dimensional settings where standard assumptions-like sparsity or data homogeneity-may not apply. The first part focuses on high-dimensional causal inference without sparsity. We analyze cross-fitted estimators and derive the asymptotic distribution of the cross-fitted augmented inverse probability weighting (AIPW) estimator under a proportional asymptotics regime. Our results highlight how cross-fitting and regularization impact estimation risk, enabling more accurate inference even in dense, high-dimensional designs. The second part addresses predictive inference under distributional heterogeneity. Classical conformal methods assume identically distributed data, an assumption violated in many real-world applications. We propose conformal algorithms for multi-environment settings, offering valid prediction intervals under minimal assumptions. These methods apply to both regression and classification, support general loss functions, and can incorporate auxiliary information to reduce interval size without compromising coverage. Together, these results advance uncertainty quantification in modern, complex data environments.

English

ISBN: 9798280719040Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Conformal prediction

Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings /
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245 1 0 $a Uncertainty and Risk Quantification in High-Dimensional Statistics: Methods for Non-Traditional Settings / $c Kuanhao Jiang.
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500 $a Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
500 $a Advisors: Sur, Pragya Committee members: Meng, Xiao-Li; Lu, Yue.
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520 $a High-dimensional data are increasingly common across fields such as genomics, economics, and neuroscience, often challenging conventional statistical methods. This dissertation develops new tools for uncertainty quantification in high-dimensional settings where standard assumptions-like sparsity or data homogeneity-may not apply. The first part focuses on high-dimensional causal inference without sparsity. We analyze cross-fitted estimators and derive the asymptotic distribution of the cross-fitted augmented inverse probability weighting (AIPW) estimator under a proportional asymptotics regime. Our results highlight how cross-fitting and regularization impact estimation risk, enabling more accurate inference even in dense, high-dimensional designs. The second part addresses predictive inference under distributional heterogeneity. Classical conformal methods assume identically distributed data, an assumption violated in many real-world applications. We propose conformal algorithms for multi-environment settings, offering valid prediction intervals under minimal assumptions. These methods apply to both regression and classification, support general loss functions, and can incorporate auxiliary information to reduce interval size without compromising coverage. Together, these results advance uncertainty quantification in modern, complex data environments.
546 $a English
590 $a School code: 0084
650 4 $a Statistics. $3 517247
650 4 $a Statistical physics. $3 536281
650 4 $a Computer science. $3 523869
653 $a Conformal prediction
653 $a Cross-fitting estimators
653 $a Hierarchical sampling
653 $a High-dimensional causal inference
653 $a Conformal algorithms
690 $a 0463
690 $a 0984
690 $a 0217
710 2 $a Harvard University. $b Statistics. $e degree granting institution. $3 3819307
720 1 $a Sur, Pragya $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-12B.
790 $a 0084
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32002503