Residual bootstraps for regression model validation

Validation is a useful and necessary part of the model-building process, identification of one or several “good” regression models is not the end of the model-building process and these models must be evaluated by various diagnostic procedures before the final regression model is determined. Residual Bootstrap method in regression model validation accomplish the goal of constructing appropriate sampling distributions empirically using the data at hand instead of statistician relying on theoretical sampling distributions like the normal, t and f where appropriateness for any given problem always rest on untestable assumptions. Validation statistics of interest such as standard error (SE), mean square error (MSE) and coefficient of determination (2) were used as criteria for selecting the best model suitable for predictive purposes.The research work concluded that to reduce the problem of overffited models in regression analysis, residual bootstrap approach should be employed in checking the validation of regression model as it gives a better estimates and stable value of coefficient of determination

PEMILIHAN HUBUNGAN INPUT-NODE PADA JARINGAN SARAF FUNGSI RADIAL BASIS

Model jaringan saraf fungsi radial basis (Radial Basis Function Neural Network = RBFNN) adalah model jaringan saraf dengan satu unit dalam lapisan tersembunyi, dimana fungsi aktivasinya adalah fungsi basis (Gaussian) dan fungsi linear pada lapisan output. Untuk mendapatkan model RBFNN terbaik, diperlukan kombinasi yang tepat antara jumlah variabel input, jumlah node (kluster), yang berimplikasi pada jumlah parameter optimal. Untuk menda-patkan sejumlah node yang diinginkan dilakukan dengan mengelompokkan data. Salah satu metode pengelompokan data adalah metode K-mean. Dengan terbentuknya kelompok data, maka nilai tengah dan varians variabel input pada setiap kluster dapat dihitung. Komponen invers varians pada fungsi aktivasi RBFNN merupakan bobot dari suatu pergeseran, sehingga diperlukan nilai interval untuk varians tersebut. Nilai varians suatu variabel input pada suatu node yang berada diluar diluar interval mengindikasikan hubungan input dengan node tidak memberi sumbangan yang signifikan pada model RBFNN, sehingga perlu dihapus. Penentuan model terbaik dari RBFNN dapat diketahui dengan kriteria nilai Mean Square Error (MSE) kecil dan Koefisien Determinasi (R2) besar. Kata kunci : RBFNN, K-mean, Interval Varians, MSE, R

Stakeholder attributes and approaches in natural disaster risk management in the built environment: the case of flood risk management in transport infrastructure

The increasing number of natural disasters has demonstrated the importance of natural disaster risk management. There is little consensus regarding the role of stakeholder attributes in reducing flood damage and explaining stakeholder approaches. Local Councils are important stakeholders in flood risk management in transport infrastructure. Hence, the characteristics of floods, Local Councils’ stakeholder attributes, and the exposure and vulnerability of the socio-economic and transport infrastructure were contextualised to examine flood damage and Local Councils’ proactive and reactive approaches. This study examines three dominant Local Councils’ stakeholder attributes of power, legitimacy and urgency by focusing on flood damage and Local Councils’ proactive and reactive approaches. Data was collected from historical archive databases and a structured questionnaire survey involving Local Councils in New South Wales, Australia that covered the time period from 1992 to 2012. This data was analysed using multi-attribute decision-making and structural equation modelling with partial least square estimation approaches. The results show that the exposure and vulnerability of Australian states and territories to flood damage depend on both socio-economic and built environment conditions. The greater the flood characteristics such as frequency, severity and type, the greater the flood damage. The exposure and vulnerability of socio-economic and transport infrastructure of a Local Council have mediating effects on the direct relationship between their stakeholder attributes and flood damage. Proactive and reactive approaches by Local Councils are highly affected by stakeholder attributes. The developed stakeholder disaster response index shows that Local Councils have practised more reactive approaches than proactive approaches. Policy makers might use the stakeholder disaster response index through continuous assessment of proactive and reactive approaches to achieve a high level of flood risk management

Stakeholder attributes and approaches in natural disaster risk management in the built environment: the case of flood risk management in transport infrastructure

The increasing number of natural disasters has demonstrated the importance of natural disaster risk management. There is little consensus regarding the role of stakeholder attributes in reducing flood damage and explaining stakeholder approaches. Local Councils are important stakeholders in flood risk management in transport infrastructure. Hence, the characteristics of floods, Local Councils’ stakeholder attributes, and the exposure and vulnerability of the socio-economic and transport infrastructure were contextualised to examine flood damage and Local Councils’ proactive and reactive approaches. This study examines three dominant Local Councils’ stakeholder attributes of power, legitimacy and urgency by focusing on flood damage and Local Councils’ proactive and reactive approaches. Data was collected from historical archive databases and a structured questionnaire survey involving Local Councils in New South Wales, Australia that covered the time period from 1992 to 2012. This data was analysed using multi-attribute decision-making and structural equation modelling with partial least square estimation approaches. The results show that the exposure and vulnerability of Australian states and territories to flood damage depend on both socio-economic and built environment conditions. The greater the flood characteristics such as frequency, severity and type, the greater the flood damage. The exposure and vulnerability of socio-economic and transport infrastructure of a Local Council have mediating effects on the direct relationship between their stakeholder attributes and flood damage. Proactive and reactive approaches by Local Councils are highly affected by stakeholder attributes. The developed stakeholder disaster response index shows that Local Councils have practised more reactive approaches than proactive approaches. Policy makers might use the stakeholder disaster response index through continuous assessment of proactive and reactive approaches to achieve a high level of flood risk management

Fast bootstrap methodology for regression model selection

Using resampling methods like cross-validation and bootstrap is a necessity in neural network design, for solving the problem of model structure selection. The bootstrap is a powerful method offering a low variance of the model generalization error estimate. Unfortunately, its computational load may be excessive when used to select among neural networks models of different structures or complexities. This paper presents the fast bootstrap (FB) methodology to select the best model structure; this methodology is applied here to regression tasks. The fast bootstrap assumes that the computationally expensive term estimated by the bootstrap, the optimism, is usually a smooth function (low-order polynomial) of the complexity parameter. Approximating the optimism term makes it possible to considerably reduce the necessary number of simulations. The FB methodology is illustrated on multi-layer perceptrons, radial-basis function networks and least-square support vector machines. (c) 2004 Published by Elsevier B.V