Predictive Inference: An Introduction

Geisser, Seymour

doi:10.1007/978-1-4899-4467-2

Cited by 948 publications

(622 citation statements)

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“…We also belief that interpretation of probabilities is easier than interpretation of confidence results. Nevertheless, as combinatorical computations for our results often coincide with those for classical frequentist methods, as indicated for some resultR in section 3, our results could also be interpreted from classical frequentist perspectives, in the same way as A(n) itself can be interpreted purely frequentistically [11].…”

Section: Low Structure Inferencesupporting

confidence: 82%

Nonparametric Predictive Inference in Statistical Process Control

Arts

Coolen

Laan

2004

Quality Technology & Quantitative Management

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Section: Low Structure Inferencesupporting

confidence: 82%

Nonparametric Predictive Inference in Statistical Process Control

Arts

Coolen

Laan

2004

Quality Technology & Quantitative Management

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“…Two standard metrics are used to quantify the accuracy of prediction algorithms: area under the receiver operating characteristic curve (AUC) [26] and Precision [27,28]. In principle, a link prediction algorithm provides an ordered list of all non-observed links (i.e., U − E T ) or equivalently gives each non-observed link, say (x, y) ∈ U − E T , a score s xy to quantify its existence likelihood.…”

Section: Problem Description and Evaluation Metricsmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,384

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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“…It fits rather naturally with the low-structure environment for which PLS was invented, with its soft or fuzzy relationships between (composite) variables. See e. g. S. Geisser (1993) and T. Hastie et al (2002) for cross-validation techniques and analyses. Cross-validation was embraced early by Herman Wold.…”

Section: Potentially Useful Constraintsmentioning

confidence: 99%