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Sampling Strategies for Computer Experiments: Design and Analysis
Lin, Dennis K.J.,Simpson, Timothy W.,Chen, Wei The Korean Reliability Society 2001 International Journal of Reliability and Applicati Vol.2 No.3
Computer-based simulation and analysis is used extensively in engineering for a variety of tasks. Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations limit their use in important areas like design optimization and reliability analysis. Statistical approximation techniques such as design of experiments and response surface methodology are becoming widely used in engineering to minimize the computational expense of running such computer analyses and circumvent many of these limitations. In this paper, we compare and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity. The first example involves the analysis of a two-member frame that has three input variables and three responses of interest. The second example simulates the roll-over potential of a semi-tractor-trailer for different combinations of input variables and braking and steering levels. Detailed error analysis reveals that uniform designs provide good sampling for generating accurate approximations using different sample sizes while kriging models provide accurate approximations that are robust for use with a variety of experimental designs and sample sizes.
Winner’s Curse in Initial Public Offering Subscriptions with Investors’ Withdrawal Options
Anlin Chen,Dennis K. J. Lin,Lanfeng Kao 한국증권학회 2010 Asia-Pacific Journal of Financial Studies Vol.39 No.1
Contrary to fixed-priced initial public offering (IPO) subscribers in many other countries, IPO subscribers in Taiwan own the option to withdraw from their IPO allocations after learning the allocation rate (ALLOC). Investors’ option to withdraw reduces the information asymmetry between informed investors and uninformed investors but increases the firmcommitment underwriting risk. We show that under investors’ option to withdraw, uninformed investors can improve their performance by learning from the ALLOC and ⁄ or the withdrawal rate. Consequently, firm-commitment underwriters will absorb more overpriced shares. Unless underwriters are compensated directly by issuers, IPOs should be more underpriced to compensate underwriting activities under investors’ option to withdraw.