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The purpose of this paper is to efficiently use as few sample points as possible to get a sufficiently explored design space and an accurate optimum for adaptive metamodel-based design optimization (AMBDO).

A parameterized lower confidence bounding (PLCB) scheme is proposed in which a cooling strategy is introduced to guarantee the balance between exploitation and exploration by varying weights of the predicting error and optimum of a metamodel. The proposed scheme is investigated by a set of test functions and a structural optimization problem, in which PLCB with four kinds of cooling control functions are studied. Moreover, other infill criteria (such as expected improvement and its extension versions) are taken into comparison.

Results show that the proposed PLCB (especially PLCB with the first cooling control function) based AMBDO method can find the optimum with fewer evaluations and maintain good accuracy, which means the proposed PLCB contributes to the excellent efficiency and accuracy in finding global optimal solutions.

The parameterized version of the lower confidence bound metric is proposed for AMBDO, typically used in the context of adaptive sampling in efficient global optimization.

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

CV-LCB approach can balance the exploration and exploitation objectively.

The aim of this paper is to consider the effect of gauge measurement capability on the multivariate process capability index (MC_p).

With respect to measurement capability, the paper investigates the statistical properties of the estimated MC_p and considers the effect of gauge measurement capability on the lower confidence bound, hypothesis testing, critical value and power of testing for MC_p at the mentioned state.

The results show that gauge measurement capabilities will notably change the results of estimating and testing the process capability index.

The research would help quality experts to determine whether their processes meet the required capability, and to make more reliable decisions.

Introduces a process capability index which indicates the incapability of a process to meet its specifications. This incapability index is obtained by a transformation of the C*pmindex, and it is more informative than C*pm. This transformation allows one to separate information concerning the process variation from information concerning the departure from the process target. As a result, departure from the process target can be assessed without process variation, and vice versa. Presents a number of practical estimators and confidence bounds (intervals).

The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided.

The purpose of this paper is to provide classification confidence value to every individual sample classified by decision trees and use this value to combine the classifiers.

The proposed system is first theoretically explained, and then the use and effectiveness of the proposed system is demonstrated on sample datasets.

In this paper, a novel method is proposed to combine decision tree classifiers using calculated classification confidence values. This confidence in the classification is based on distance calculation to the relevant decision boundary (distance conditional), probability density estimation and (distance conditional) classification confidence estimation. It is shown that these values – provided by individual classification trees – can be integrated to derive a consensus decision.

The proposed method is not limited to axis‐parallel trees, it is applicable not only to oblique trees, but also to any kind of classifier system that uses hyperplanes to cluster the input space.

A novel method is presented to extend decision tree like classifiers with confidence calculation and a voting system is proposed that uses this confidence information. The proposed system possesses several novelties (e.g. it not only gives class probabilities, but also classification confidences) and advantages over previous (traditional) approaches. The voting system does not require an auxiliary combiner or gating network, as in the mixture of experts structure and the method is not limited to decision trees with axis‐parallel splits; it is applicable to any kind of classifiers that use hyperplanes to cluster the input space.

It is understood that the personality traits and intelligence levels of the tourist guides directly or indirectly affect their ability to solve the problems they encounter on tours. This study aims to test whether emotional intelligence has an intermediary effect on the problem-solving skills of professional tourist guides with perfectionist personality traits.

Field research was conducted within the study to analyze suggestions on the interaction of variables on an empirical basis, and data were collected using interview, document review and survey technique. Thus, the mixed-methods approach was used in the study. Within the scope of this study in which 410 professional tourist guides were surveyed, a substantial part of the research data was obtained through the application of the survey technique. Besides, interviews were carried out with 12 professional tourist guides. The clues obtained by the qualitative study were transformed into hypotheses within the scope of the quantitative study, and the intermediary effect was tested.

A relationship between the main themes, sub-themes and codes was determined within the framework of the qualitative method. As a result of the mediation test, it has been revealed that emotional intelligence has an intermediary role in the relationship between perfectionism and problem-solving skills. According to the results of bootstrapping, the indirect effect of emotional intelligence on perfectionism and problem-solving skills was found out to be significant.

The study acknowledged that positive perfectionism, high emotional intelligence and problem-solving skills contributed to the professional tourist guides being willing to provide better service. In tune with the assumption that the more the quality of the tours carried out through agencies increases, the more satisfied tourists are; the study implicated that it would be advisable for agencies to prioritize the trainings provided for their tour guides to enhance their positive perfectionist, emotionally intelligent personalities and problem-solving skills. Given that professional tourist guides may create a positive country image with the quality service they provide, the significance of such trainings stretch beyond the benefits of such organizations.

Relevant variables were analyzed with a mixed method and applied on professional tourist guides.

This article studies formal optimal decision approaches for a multi‐period asset/liability management model for a pension fund. The authors use Conditional Value‐at‐Risk (CVaR) as a risk measure, the weighted average of the Value‐at‐Risk (VaR) and those losses exceeding VaR. The model is based on sample‐path simulation of the liabilities and returns of financial instruments in the portfolio. The same optimal decisions are made for groups of sample‐paths, which exhibit similar performance characteristics. Since allocation proportions are time‐dependent, these techniques are more flexible than more standard allocation procedures, e.g. “constant proportions.” Optimization is conducted using linear programming. Compared with traditional stochastic programming algorithms (for which the problem dimension increases exponentially in the number of time stages), this approach exhibits a linear growth of the dimension. Therefore, this approach allows the solution of problems with very large numbers of instruments and scenarios.

Research has shown evidence of the use of impression management strategies in corporate disclosures as a means of presumably tempering and swaying investors’ perceptions. These impression management strategies include shifts in the tone used when providing disclosures. However, recent research also provides evidence that such techniques can have a contrary effect when the tone of the message appears to be “too good to be true.” This study explores how the use of optimism and certainty in the Management Discussion and Analysis (MD&A) portion of the annual report affects nonprofessional investors’ investment decisions – a class of investors known to heavily rely on the MD&A portion of annual reports. We theorize a bifurcated effect where optimism and certainty have a positive and direct effect on investor willingness to invest, but at the same time optimism and certainty have a negative indirect effect on willingness to invest that is mediated through decreased perceptions of disclosure credibility. The results provide evidence supporting such a bifurcated effect from the use of tone in management disclosures.

The insurance industry, in general, accepts large risks due to the combined severity and frequency of catastrophic events; further, these risks are poorly defined given the small amount of data available for extreme events. It is important for the equitable transfer of risk to understand and quantify this risk as accurately as possible. As this risk is propagated to the capital markets, more and more parties will be exposed. An important part of pricing insurance‐linked securities (ILS) is quantifying the uncertainties existing in the physical parameters of the catastrophe models, including both the hazard and damage models. Given the amount of reliable data (1945 till present) on important storm parameters such as central pressure drop, radius to maximum winds, and non‐stationarity of the occurrence rate, moments estimated for these parameters are not highly reliable and knowledge uncertainty must be considered. Also, the engineering damage model for a given class of building in a large portfolio is subject to uncertainty associated with the quality of the buildings. A sample portfolio is used to demonstrate the impact of these knowledge uncertainties. Uncertainties associated with variability of statistics on central pressure drop, occurrence rate, and building quality were estimated and later propagated through a tropical cyclone catastrophe model to quantify the uncertainty of PML results. Finally their effect on the pricing of a typical insurance‐linked security (ILS) was estimated. Statistics of spread over LIBOR given different bond ratings/probability of attachment are presented using a pricing model (Lane [2000]). For a typical ILS, a relatively large coefficient of variation for both probability of attachment and spread over LIBOR was observed. This in turn leads to a rather large price uncertainty for a typical layer and may explain why rational investors expect a higher return for assuming catastrophe risk. The results hold independent of pricing model used. The objective of this study is to quantify this uncertainty for a simple call option and demonstrate its effect on pricing.