Bootstrap Standard Error and Confidence Intervals for the Correlation Corrected for Range Restriction: A Simulation Study.

The standard Pearson correlation coefficient is a biased estimator of the true population correlation, ρ, when the predictor and the criterion are range restricted. To correct the bias, the correlation corrected for range restriction, rc, has been recommended, and a standard formula based on asymptotic results for estimating its standard error is also available. In the present study, the bootstrap standard-error estimate is proposed as an alternative. Monte Carlo simulation studies involving both normal and nonnormal data were conducted to examine the empirical performance of the proposed procedure under different levels of ρ, selection ratio, sample size, and truncation types. Results indicated that, with normal data, the bootstrap standard-error estimate is more accurate than the traditional estimate, particularly with small sample size. With nonnormal data, performance of both estimates depends critically on the distribution type. Furthermore, the bootstrap bias-corrected and accelerated interval consistently provided the most accurate coverage probability for ρ. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Subjective Probability Intervals: How to Reduce Overconfidence by Interval Evaluation.

Format dependence implies that assessment of the same subjective probability distribution produces different conclusions about over- or underconfidence depending on the assessment format. In 2 experiments, the authors demonstrate that the overconfidence bias that occurs when participants produce intervals for an uncertain quantity is almost abolished when they evaluate the probability that the same intervals include the quantity. The authors successfully apply a method for adaptive adjustment of probability intervals as a debiasing tool and discuss a tentative explanation in terms of a naive sampling model. According to this view, people report their experiences accurately, but they are naive in that they treat both sample proportion and sample dispersion as unbiased estimators, yielding small bias in probability evaluation but strong bias in interval production. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Defuzzification as a special way of dealing with retranslation

The problem of defuzzification is examined in this paper from a broader perspective as a special way of dealing with the general problem of retranslation. The paper includes an overview of different formulations of the problem of defuzzification, as well as an overview of methods that have been suggested in the literature for dealing with the problem. Our own approach to defuzzification, which is described in the paper in more details, is based on relevant measures of uncertainty-based information. This paper is a companion to our recent paper that addresses the general problem of retranslation in computing with perceptions (Martin and Klir 2006).     

A novel approach for coal seam terrain prediction through information fusion of improved D–S evidence theory and neural network

In order to effectively and accurately forecast the distribution of coal seam terrain, a novel prediction approach through information fusion of improved D–S evidence theory and neural network was proposed. An improved strategy based on confidence level was presented for evidence theory to reduce the conflicts between evidences and enhance the fusion effect. Moreover, BPAs function was constructed reasonably through extracting weights from preliminary prediction values of four neural networks, and the flowchart of proposed approach was designed. Furthermore, a simulation example was provided and some comparisons with other fusion prediction methods were carried out. The simulation example and comparison results indicated that the proposed approach was feasible, high-precision and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to specify the effect of proposed system.     

Confidence interval optimization for testing hypotheses under data with low statistics

The notion of a confidence interval (CI) is used in the both major sections of the mathematical statistics: parameter estimation and hypothesis testing. While estimating parameters a CI defines the bounds of the parameter estimates with a certain confidence probability, and while testing hypotheses a CI defines with a certain confidence probability an interval of values of the random variable which do not contradict the tested hypothesis about them.     

Metacognition and system usability: Incorporating metacognitive research paradigm into usability testing

There is an agreement that perceived usability is important beyond actual effectiveness of software systems. Perceived usability is often obtained by self-reports provided after system use. Aiming to improve summative usability testing, we propose a methodology to enhance in-depth testing of users' performance and perceived usability at the task level. The metacognitive research approach allows detailed analysis of cognitive processes. Adapting its methodologies, we propose the Metacognitive Usability Profile (MUP) which includes a comprehensive set of measures based on collecting confidence in the success of each particular task and triangulating it with objective measures. We demonstrate using the MUP by comparing two versions of a project management system. Based on a task analysis we allocated tasks that differ between the versions and let participants (N = 100) use both versions. Although no difference was found between the versions in system-level perceived usability, the detailed task-level analysis exposed many differences. In particular, overconfidence was associated with low performance, which suggests that user interfaces better avoid illusions of knowing. Overall, the study demonstrates how the MUP exposes challenges users face. This, in turn, allows choosing the better task implementation among the examined options and to focus attempts for usability improvement.     

煤基SN级5W-30汽油机油行车试验研究

行车试验是全面评价汽油机油（润滑油）性能的重要方法。分别对自研煤基5W-30汽油机油和市售SN 5W-30汽油机油进行10 000 km行车试验研究，依据国家标准GB/T 8028—2010《汽油机油换油指标》对不同行驶里程下的旧油进行理化性能分析。结果表明：10 000 km行车试验后，2种汽油机油性能指标均未超过换油指标要求，仍有较大性能余量；相比市售SN 5W-30汽油机，自研煤基汽油机油行车试验后铁含量和酸值增加更小，碱值余量更大，表现出更优异的润滑性能、抗氧化性能、抗磨损性能和清洁分散性能。     

Use of a quantile regression based echo state network ensemble for construction of prediction Intervals of gas flow in a blast furnace

The usual huge fluctuations in the blast furnace gas (BFG) generation make the scheduling of the gas system become a difficult problem. Considering that there are high level noises and outliers mixed in original industrial data, a quantile regression-based echo state network ensemble (QR-ESNE) is modeled to construct the prediction intervals (PIs) of the BFG generation. In the process of network training, a linear regression model of the output matrix is reported by the proposed quantile regression to improve the generalization ability. Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles. To verify the performance of the proposed method, the practical data coming from a steel plant are employed, and the results indicate that the proposed method exhibits high accuracy and reliability for the industrial data. Furthermore, an application software system based on the proposed method is developed and applied to the practice of this plant.     

Load interaction effects in propagation lifetime and inspections of railway axles

As well known, an interaction effect arises, on crack propagation, when a specimen or a component is subjected to variable amplitude fatigue loading. Depending on the applied load sequence, a certain amount of retardation or acceleration can then be observed, on the fatigue crack growth rate, with respect to the constant amplitude case. In the case of structural ductile materials, the interaction phenomenon is mainly addressed by the local plasticity at the crack tip and can be explained, from a global point of view, by adopting the crack closure concept. In the present research, load interaction effects in a medium strength steel for railway axles are experimentally analyzed by companion and full-scale specimens. The experimental outcomes show a significant retardation with respect to a simple no-interaction approach and the Strip-Yield model offers good, yet conservative, estimates of crack advance. The consequences of crack growth retardation on the inspection periodicity of railway axles are then discussed.     

Quick Method for Choosing a Transf orrnation

The choice of non-linear transformations in the analysis of data can frequently be simplified by restricting the possible transformations to a particular family. Tukey has shown that the “simple family” has many desirable properties from this point of view. This family can be represented as the set of solutions to a third order differential equation and the constant of this equation provides a convenient index of the family. This index may be approximated by substituting the given data into the corresponding difference equation. The resulting approximation can then be used for rough solutions or as a starting value for the iterative solution of the maximum likelihood equations given by Turner. Two examples are provided to demonstrate the procedure.