• Geomechanics and Engineering
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• 제33권1호
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Journal of the Korean Statistical Society
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• 제20권2호
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• pp.118-138
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• 1991

Given a random sample of size N(unknown) with density f(x $\theta$), suppose that only n observations which lie outside a region R are recorded. On the basis of n observations, the Bayes estimators of $\theta$ and N are considered and their asymptotic expansions are developed to compare their second order asymptotic properties with those of the maximum likelihood estimators and the Bayes modal estimators. Corrections to bias and median bias of these estimators are made. An example is given to illustrate the results obtained.

• Communications for Statistical Applications and Methods
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• 제21권1호
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• pp.93-103
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• 2014

This paper deals with the problem of predicting censored data in a half triangle distribution with an unknown parameter based on progressively Type-II censored samples. We derive maximum likelihood predictors and some approximate maximum likelihood predictors of censored failure times in a progressively Type-II censoring scheme. In addition, we construct the shortest-length predictive intervals for censored failure times. Finally, Monte Carlo simulations are used to assess the validity of the proposed methods.

• Communications for Statistical Applications and Methods
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• 제15권5호
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• pp.765-774
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• 2008

In this paper, we derive the approximate maximum likelihood estimators(AMLEs) and maximum likelihood estimator of the scale parameter in a triangular distribution based on progressive Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation for various progressive censoring schemes.

• Communications for Statistical Applications and Methods
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• 제5권2호
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• pp.411-416
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• 1998

A simple nonparametric test of complete or total independence is suggested for continuous multivariate distributions. This procedure first discretizes the original variables based on their order statistics, and then tests the hypothesis of complete independence for the resulting contingency table. Under the hypothesis of independence, the chi-squared test statistic has an asymptotic chi-squared distribution. We present a simulation study to illustrate the accuracy in finite samples of the limiting distribution of the test statistic. We compare our method to another nonparametric test of complete independence via a simulation study. Finally, we apply our method to the residuals from a real data set.

• 응용통계연구
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• 제1권2호
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• pp.61-65
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• 1987

확률화(randomization)는 각 실험단위를 처리에 배치할 때 반드시 지켜야 할 실험계획의 큰 원칙이다. 확률화는 각 처리 간의 비교에 있어 실험외적 요인의 효과를 상쇄시켜 주며 또한 통계적 추론을 더욱 뒷받침하여 준다.

• Journal of the Korean Statistical Society
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• 제36권2호
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• pp.299-320
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• 2007

We introduce a high breakdown point estimator referred to as data partitioning robust regression estimator (DPR). Since the DPR is obtained by partitioning observations into a finite number of subsets, it has no computational problem unlike the previous robust regression estimators. Empirical and extensive simulation studies show that the DPR is superior to the previous robust estimators. This is much so in large samples.

• Journal of the Korean Data and Information Science Society
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• 제14권1호
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• pp.83-92
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• 2003

In this paper, we derive noninformative priors for the ratio of parameters in the Burr model. We obtain Jeffreys' prior, reference prior and second order probability matching prior. Also we prove that the noninformative prior matches the alternative coverage probabilities and a HPD matching prior up to the second order, respectively. Finally, we provide simulated frequentist coverage probabilities under the derived noninformative priors for small and moderate size of samples.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.683-696
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• 2005

The results of using principal coordinates prior to cluster analysis are investigated on the samples from multiple binary outcomes. The retrieval ability of the known clustering algorithm is significantly improved by using principal coordinates instead of using the distance directly transformed from four association coefficients for multiple binary variables.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.161-168
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• 1996

This paper describes a sampling method, which can be used instead of the simple random sampling without replacement(SRSWOR). This method, circular sampling, assumes that the sampling units of the population are arranged in circular format, and randomly selects as many as samples of contiguous units. Therefore this method gathers information quicker and easier than STSWOR. In certain circumstances, the reliability of this method is better than that of STSWOR. And of circular sampling would be applied to nonprobability could be determined. methods, the reliability of the sample results in terms of probability could be determined.