• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.1-15
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• 2007

Consider a Rayleigh distribution with $$pdf\;p(x/{\theta})\;=\;2x{\theta}^{-1}\;{\exp}\;({-x^2}/{\theta})$$ and mean lifetime ${\mu}\;=\;\sqrt{\pi\theta}/2$. We study the two-action problem of testing the hypotheses $H_{0}\;:\;{\mu}{\leq}{\mu}_{0}$ against $H_{1}\;:\;{\mu}\;>\;{\mu}_{0}$ using a linear error loss of ${\mid}{\mu}\;-\;{\mu}_{0}{\mid}$ via the empirical Bayes approach. We construct a monotone empirical Bayes test ${\delta}^{*}_{n}$ and study its associated asymptotic optimality. It is shown that the regret of ${\delta}^{*}_{n}$ converges to zero at a rate $\frac{{\ln}^{2}n}{n}$, where n is the number of past data available when the present testing problem is considered.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.11
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• pp.1511-1514
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• 2012

Epistasis that may explain a large portion of the phenotypic variation for complex economic traits of animals has been ignored in many genetic association studies. A Baysian method was introduced to draw inferences about multilocus genotypic effects based on their marginal posterior distributions by a Gibbs sampler. A simulation study was conducted to provide statistical powers under various unbalanced designs by using this method. Data were simulated by combined designs of number of loci, within genotype variance, and sample size in unbalanced designs with or without null combined genotype cells. Mean empirical statistical power was estimated for testing posterior mean estimate of combined genotype effect. A practical example for obtaining empirical statistical power estimates with a given sample size was provided under unbalanced designs. The empirical statistical powers would be useful for determining an optimal design when interactive associations of multiple loci with complex phenotypes were examined.

• Geomechanics and Engineering
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• v.30 no.4
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• pp.393-399
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• 2022

The Segregation Potential (SP) is one of the most widely used predictors of frost heave in cold regions. Laboratory step-freezing tests determining a representative SP at the onset of the formation of the last ice lens (near the thermal steady state condition) can predict susceptibility to frost heave. Previous work has proposed empirical semi-log fitting for determination of the representative SP and applied it to several fine-grained soils, but considering only frost-susceptible soils. The presence of fines in coarse-grained soil affects frost susceptibility. Therefore, it is required to evaluate the applicability of the empirical semi-log fitting for both frost-susceptible and non-frost-susceptible soils with fines content. This paper reports laboratory frost heave tests for fines contents of 5%-70%. The frost susceptibility of soil mixtures composed of sand and silt was classified by the representative SP, and the suitability of the empirical semi-log fitting method was assessed. Combining semi-log fitting with simple laboratory frost heave testing using a temperature-controllable cell is shown to be suitable for both frost-susceptible and non-frost-susceptible soils. In addition, initially non-frost-susceptible soil became frost susceptible at a 10%-20% weight fraction of fines. This threshold fines content matched well with transitions in the engineering characteristics of both the unfrozen and frozen soil mixtures.

• ETRI Journal
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• v.31 no.2
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• pp.140-150
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• 2009

Although mutation testing is potentially powerful, it is a computationally expensive testing method. To investigate how we can reduce the cost of object-oriented mutation testing, we have conducted empirical studies on class mutation operators. We applied class mutation operators to 866 classes contained in six open-source programs. An analysis of the number and the distribution of class mutants generated and preliminary data on the effectiveness of some operators are provided. Our study shows that the overall number of class mutants is smaller than for traditional mutants, which offers the possibility that class mutation can be made practically affordable.

• Wind and Structures
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• v.23 no.5
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• pp.385-403
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• 2016

The across-wind dynamic loads on L-shaped tall buildings with various geometric dimensions were investigated through a series of wind tunnel testing. The lift coefficients, power spectral densities and vertical correlation coefficients of the across-wind loads were analyzed and discussed in details. Taking the side ratio and terrain category as key variables, empirical formulas for estimating the across-wind dynamic loads on L-shaped tall buildings were proposed on the basis of the wind tunnel testing results. Comparisons between the predictions by the empirical formulas and the wind tunnel test results were made to verify the accuracy and applicability of the proposed formulas. Moreover, a simplified procedure to evaluate the across-wind dynamic loads on L-shaped tall buildings was derived from the proposed formulas. This study aims to provide a simple and reliable way for the estimation of across-wind dynamic loads on L-shaped tall buildings.

• Computers and Concrete
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• v.17 no.4
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• pp.489-498
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• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.137-141
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• 2009

We study on selecting the kernel weighting function in smoothing moment conditions for dependent processes. For hypothesis testing in Generalized Method of Moments or Generalized Empirical Likelihood context, we find that smoothing moment conditions by Bartlett kernel delivers smallest size distortions based on empirical Edgeworth expansions of the long-run variance estimator.

• Journal of Distribution Science
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• v.17 no.10
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• pp.17-26
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• 2019

Purpose - This research investigates the old marketing mix approach to satisfaction and loyalty from the perspective of research subjects of Samsung brand smartphone cases through empirical testing involving product quality, price, distribution channel variables as antecedent variables. Research design, data, and methodology - This study emphasizes the empirical/quantitative concept by using a survey as a data collection tool. The number of samples used was 179 eligible respondents who used Samsung smartphone devices for more than five years. Statistical testing tools use PLS with several testing stages such as the classical assumption test to the hypothesis testing stage. Results - The nine hypotheses proposed, as many as two hypotheses were proposed, namely intervening relationships involving Price and Distribution channel variables on customer satisfaction and customer loyalty. Conclusions - Product quality is the essential component affecting customer satisfaction and loyalty while distribution channel is a complementary component that is no less important to measure the extent to which customer satisfaction expectations and customer loyalty are realized for the product quality of the products that have been produced and marketed. The price component is not the only reason to make consumers satisfied and loyal.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.213-228
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• 2008

Using a linear loss function, this paper considers the one-sided testing problem for the exponential distribution via the empirical Bayes(EB) approach. Based on right censored data, we propose an EB test for the exponential parameter and obtain its convergence rate and asymptotic optimality, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.136-136
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• 2022

This study investigates the possibility of coupling empirical mode decomposition (EMD) for runoff prediction from machine learning (ML) models. Here, support vector regression (SVR) and convolutional neural network (CNN) were considered for ML algorithms. Precipitation (P), minimum temperature (Tmin), maximum temperature (Tmax) and their intrinsic mode functions (IMF) values were used for input variables at a monthly scale from Jan. 1973 to Dec. 2020 in the Grand river basin, Canada. The support vector machine-recursive feature elimination (SVM-RFE) technique was applied for finding the best combination of predictors among input variables. The results show that the proposed method outperformed the individual performance of SVR and CNN during the training and testing periods in the study area. According to the correlation coefficient (R), the EMD-SVR model outperformed the EMD-CNN model in both training and testing even though the CNN indicated a better performance than the SVR before using IMF values. The EMD-SVR model showed higher improvement in R value (38.7%) than that from the EMD-CNN model (7.1%). It should be noted that the coupled models of EMD-SVR and EMD-CNN represented much higher accuracy in runoff prediction with respect to the considered evaluation indicators, including root mean square error (RMSE) and R values.