Gaussian Process Metamodeling in Bayesian Value of Information Analysis: A Case of the Complex Health Economic Model for Breast Cancer Screening

Rojnik, Klemen; Naveršnik, Klemen

doi:10.1111/j.1524-4733.2007.00244.x

Cited by 21 publications

(32 citation statements)

References 13 publications

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“…The input set of 10,000 random samples, identical to the one used in the original analysis, was reshuffled to give the required correlations and the model reanalyzed by using the Gaussian process metamodel developed earlier [49]. The resulting CEAC and EVPI are shown in Figure 6.…”

Section: Mixture Of Extremal Distributionsmentioning

confidence: 99%

Handling Input Correlations in Pharmacoeconomic Models

Naveršnik¹,

Rojnik²

2012

Value in Health

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Section: Mixture Of Extremal Distributionsmentioning

confidence: 99%

Handling Input Correlations in Pharmacoeconomic Models

Naveršnik¹,

Rojnik²

2012

Value in Health

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“…• Mechanical engineering: Slonski (2011), Lee and Gard (2014), and Dubourg et al (2013) • Health economics: Rojnik and Naveršnik (2008), and Stevenson et al (2004) • Computer and system engineering: Kennedy et al (2006), Johnson et al (2011), and Zhang et al (2017) • Chemical engineering: Gomes et al (2008), Caballero and Grossmann (2008), and Liu et al (2013) • Other fields: Kapoor et al (2010), Rohmer and Foerster (2011), Deng et al (2012), Silvestrini et al (2013), Tagade et al (2013), and Kumar (2015).…”

Section: Introductionmentioning

confidence: 99%

Model selection algorithm in Gaussian process regression for computer experiments

Lee

Park

2017

CSAM

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The model in our approach assumes that computer responses are a realization of a Gaussian processes superimposed on a regression model called a Gaussian process regression model (GPRM). Selecting a subset of variables or building a good reduced model in classical regression is an important process to identify variables influential to responses and for further analysis such as prediction or classification. One reason to select some variables in the prediction aspect is to prevent the over-fitting or under-fitting to data. The same reasoning and approach can be applicable to GPRM. However, only a few works on the variable selection in GPRM were done. In this paper, we propose a new algorithm to build a good prediction model among some GPRMs. It is a post-work of the algorithm that includes the Welch method suggested by previous researchers. The proposed algorithms select some non-zero regression coefficients (β's) using forward and backward methods along with the Lasso guided approach. During this process, the fixed were covariance parameters (θ's) that were pre-selected by the Welch algorithm. We illustrated the superiority of our proposed models over the Welch method and non-selection models using four test functions and one real data example. Future extensions are also discussed.

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“…Recently, the number of applications in health care has steadily grown. A range of studies was published after 2004 [4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%