Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Qiang Cheng; Jale Tezcan; Jie Cheng

doi:10.1016/j.patrec.2013.12.010

Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Qiang Cheng, Jale Tezcan, Jie Cheng

Internal Medicine

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

Original language	English
Pages (from-to)	88-95
Number of pages	8
Journal	Pattern Recognition Letters
Volume	40
Issue number	1
DOIs	https://doi.org/10.1016/j.patrec.2013.12.010
State	Published - Apr 15 2014

Bibliographical note

Funding Information:
This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

Funding

This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

Funders	Funder number
National Science Foundation (NSF)

Keywords

Confidence interval
Least squares support vector machine
Mixed effect modeling
Prediction interval
Semiparametric function estimation

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patrec.2013.12.010

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abstract = "We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.",

keywords = "Confidence interval, Least squares support vector machine, Mixed effect modeling, Prediction interval, Semiparametric function estimation",

author = "Qiang Cheng and Jale Tezcan and Jie Cheng",

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AU - Cheng, Qiang

AU - Tezcan, Jale

AU - Cheng, Jie

N1 - Funding Information: This study was supported by Grants, CMMI-1100735 and IIS-1218712 , from the National Science Foundation .

PY - 2014/4/15

Y1 - 2014/4/15

N2 - We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

AB - We consider estimating the confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine (LS-SVM). Explicit formulas are derived for confidence and prediction intervals. The accuracy of the derived analytical equations is assessed by comparing with wild cluster bootstrap-t method on simulated and real-world data with different levels of random-effect and residual variances, and different numbers of clusters. Close match between the derived expressions and the bootstrap results is observed.

KW - Confidence interval

KW - Least squares support vector machine

KW - Mixed effect modeling

KW - Prediction interval

KW - Semiparametric function estimation

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VL - 40

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JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

IS - 1

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Confidence and prediction intervals for semiparametric mixed-effect least squares support vector machine

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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