Dual Kriging: An Exploratory Use in Economic Metamodeling

Chaveesuk, Ravipim; Smith, Alice E.

doi:10.1080/00137910500227182

Cited by 5 publications

(7 citation statements)

References 21 publications

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“…Several empirical studies proved its superiority over other interpolating techniques such as splines (Laslett, 1994). Kriging has been used in many fields including cost estimation (Chaveesuk and Smith, 2005;Baioumy et al, 2008), simulation interpolation (Barton, 1994;Kleijnen and van Beers, 2005), and optimisation (Huang et al, 2006;Liu and Smith, 2007). Recently, Kriging has been applied to the prediction of wave propagation in space due to its accuracy advantage in performing the spatial interpolation.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of wireless network connectivity using a Taylor Kriging approach

Al-Khafaji

Smith

2011

IJAIP

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The research aim of this paper is to investigate the effectiveness of a new Kriging model which uses Taylor expansion to predict wireless network connectivity. Wireless network connectivity is measured by the strength of emitted signal power from the tower to the point in question. The prediction results are compared with those from the literature where an Ordinary Kriging model and a neural network are used to conduct the same prediction. Root mean squared error (RMSE) and maximum absolute relative error (MARE) show that the prediction results of the new Kriging model are much better than those obtained before with average differences from 51.56% to 85%. This study shows the promise of the new Kriging model to accurately estimate wireless signal strength.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of wireless network connectivity using a Taylor Kriging approach

Al-Khafaji

Smith

2011

IJAIP

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“…As we mentioned earlier, Chaveesuk and Smith (2005) used the standardized base function coefficients to perform sensitivity analysis. Note that the base functions themselves could be nonlinear functions, and particularly the function outputs are partially determined byβ C(X).…”

Section: H Liumentioning

confidence: 99%

“…Kriging is a powerful and well-proven spatial statistical technique named after Krige, a mining engineer in South Africa (Journel and Huijbregts 1978). Chaveesuk and Smith (2005) first applied the dual kriging approach to cost estimation. They observe that the dual kriging approach has the advantages of explicitness and accuracy in comparison with multiple linear regression (MLR) and ANN.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, they conduct sensitivity analysis by using the standardized base function coefficients of a drift function in kriging. However, Chaveesuk and Smith (2005) did not illustrate how to identify the base functions in kriging. This article introduces TK to solve this problem.…”

Section: Introductionmentioning

confidence: 99%

“…This article develops the partial differentiation equation of kriging and uses it to conduct sensitivity analysis on cost factors, thus providing an accurate kriging method of sensitivity analysis on cost factors. Though constructing regression metamodels to estimate cost, some references such as Smith and Mason (1997) and Chaveesuk and Smith (2005) do not consider the problems of multicollinearity, heteroskedasticity, and specification error. This article illustrates these problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cost Estimation and Sensitivity Analysis on Cost Factors: A Case Study on Taylor Kriging, Regression and Artificial Neural Networks

2010

The Engineering Economist

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This article applies Taylor kriging (TK) to cost estimation. The partial differentiation equation of TK is developed and used to assist in sensitivity analysis on cost factors. The capabilities of cost estimation and sensitivity analysis of TK are compared with those of regression and an artificial neural network (ANN) through application in a case study. The resultsshow that TK can provide more accurate cost estimates than those of regression but worse than those of ANN, and both TK and regression are able to effectively find sensitive cost factors.

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Simulation optimization based on Taylor Kriging and evolutionary algorithm

Maghsoodloo

2011

Applied Soft Computing

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Dual Kriging: An Exploratory Use in Economic Metamodeling

Cited by 5 publications

References 21 publications

Prediction of wireless network connectivity using a Taylor Kriging approach

Prediction of wireless network connectivity using a Taylor Kriging approach

Cost Estimation and Sensitivity Analysis on Cost Factors: A Case Study on Taylor Kriging, Regression and Artificial Neural Networks

Simulation optimization based on Taylor Kriging and evolutionary algorithm

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