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Boyd, D and Bentley, D (2012) A critique of conceptions of design and management in construction projects. Construction Management and Economics, 30(06), 441-54.

Bröchner, J, Josephson, P-E and Kadefors, A (2002) Swedish construction culture, quality management and collaborative practice. Building Research & Information, 30(06), 392–400.

Erman, E (2002) Timber joint design: the geometric breakdown method. Building Research & Information, 30(06), 446–69.

Jennings, W (2012) Why costs overrun: risk, optimism and uncertainty in budgeting for the London 2012 Olympic Games. Construction Management and Economics, 30(06), 455-62.

Lehtiranta, L, Kärnä, S, Junnonen, J-M and Julin, P (2012) The role of multi-firm satisfaction in construction project success. Construction Management and Economics, 30(06), 463-75.

Ofori, G (2002) Singapore's construction: moving toward a knowledge-based industry. Building Research & Information, 30(06), 401–12.

Pietroforte, R and Miller, J B (2002) Procurement methods for US infrastructure: historical perspectives and recent trends. Building Research & Information, 30(06), 425–35.

Reeves, K (2002) Construction business systems in Japan: general contractors and subcontractors. Building Research & Information, 30(06), 413–24.

Rwelamila, P D (2002) Creating an effective construction industry strategy in South Africa. Building Research & Information, 30(06), 435–45.

Shi, Q, Zuo, J and Zillante, G (2012) Exploring the management of sustainable construction at the programme level: a Chinese case study. Construction Management and Economics, 30(06), 425-40.

Yuan, X X (2012) Bayesian method for the correlated competitive bidding model. Construction Management and Economics, 30(06), 477-91.

  • Type: Journal Article
  • Keywords: bidding; decision analysis; modelling; statistical analysis; uncertainty
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446193.2012.666802
  • Abstract:
    A multivariate competitive bidding model takes into account the correlation among competitors in determination of markup size. However, parameter estimation for the multivariate model is a challenging issue. A simplified, piecemeal style statistical method was proposed for low-dimension problems. However, this method may cause significant estimation errors when applied to complex bidding situations. A refined Bayesian statistical method based on Markov chain Monte Carlo (MCMC) simulation is developed that can be employed in practical bidding problems. To deal with missing values in bid data, a data augmentation technique is integrated in the MCMC process. The proposed Bayesian method is shown through case studies to be robust for complex bidding situations and also insensitive to the selection of the prior models of the correlation matrix. An important feature of the proposed Bayesian method is that it allows a project manager to quantify statistical uncertainties of parameter estimation and their effects on markup decisions. The optimal markup is represented by a posterior distribution which paints a complete picture of the uncertainties involved in the markup size decision.