• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.923-936
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• 2021

A large number of non-responses are occurring in the sample survey, and various methods have been developed to deal with them appropriately. In particular, the bias caused by non-ignorable non-response greatly reduces the accuracy of estimation and makes non-response processing difficult. Recently, Chung and Shin (2017, 2020) proposed an estimator that improves the accuracy of estimation using parametric super-population model and response rate model. In this study, we suggested a bias corrected non-response mean estimator using a nonparametric function generalizing the form of a parametric super-population model. We confirmed the superiority of the proposed estimator through simulation studies.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.11-23
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• 2020

Sampling design should provide statistics to meet a given accuracy while saving cost and time. However, a large number of non-responses are occurring due to the deterioration of survey circumstances, which significantly reduces the accuracy of the survey results. Non-responses occur for a variety of reasons. Chung and Shin (2017, 2019) and Min and Shin (2018) found that the accuracy of estimation is improved by removing the bias caused by non-response when the response rate is an exponential or linear function of variable of interests. For that case they assumed that the error of the super population model follows normal distribution. In this study, we proposed a non-response bias adjusted estimator in the case where the error of a super population model follows the gamma distribution or the log-normal distribution in a business survey. We confirmed the superiority of the proposed estimator through simulation studies.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.409-420
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• 2020

In business survey, modified cut-off sampling is commonly used to greatly increase the accuracy of the estimation while reducing the number of samples. However, non-response rate of take-all stratum has increased significantly and the sample substitution is not possible because the non-response in the take-all stratum affects the accuracy of the estimation. It is important to adjust the bias appropriately if non-response is affected by the variable of interest. In this study, a bias adjusted estimation is proposed as an appropriate method to deal with a non-response in the take-all stratum. In particular, the estimator proposed by Chung and Shin (2020) was applied to the bias adjustment for the take-all stratum; therefore, we suggest a new method to adjust properly for the take-all stratum. The superiority of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.993-1004
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• 2017

A stratified sampling method is generally used with a sample selected using the same sample weight in each stratum in order to improve the accuracy of the sampling survey estimation. However, the weight should be adjusted to reflect the response rate if the response rate is affected by the value of the variable of interest. It may be also more effective to adjust the weights by subdividing the stratum rather than using the same weight if the variable of interest has a linear relationship with the continuous auxiliary variables. In this study, we propose a method to increase the accuracy of estimation using an informative sampling design technique when the response rate is an exponential function of the variable of interest and the variable of interest has a linear relationship with the auxiliary variable. Simulation results show the superiority of the proposed method.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.377-390
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• 2017

When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.69-82
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• 2017

R-indicator (representativeness indicator) is used to check the representativeness of samples when non-responses occur. The representativeness is related with the accuracy of parameter estimator and the accuracy is related with bias of the estimator. Hence, unbiased estimator generates high accuracy. Therefore, high value of R-indicator guarantees the accuracy of parameter estimation with a small bias. R-indicator is calculated through propensity scores obtained by logit or probit modeling. In this paper we investigate the degree of relation between R-indicator and different non-response rates in strata using simulation studies. We also analyze a modified Korea Economic Census data for real data analysis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.45-55
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• 2008

The objective of this research was to develop a VEPCD (ViscoElastoPlastic Continuum Damage) Model which is used to predict the behavior of asphalt concrete under various loading and temperature conditions. This paper presents the VEPCD model formulated in a tension mode and its validation using four hot mix asphalt (HMA) mixtures: dense-graded HMA, SBS, CR-TB, and Terpolymer. Modelling approaches consist of two components: the ViscoElastic Continuum Damage (VECD) mechanics and the ViscoPlastic (VP) theory. The VECD model was to describe the time-dependent behavior of HMA with growing damage. The irrecoverable (whether time-dependent or independent) strain has been described by the VP model. Based on the strain decomposition principle, these two models are integrated to form the VEPCD model. For validating the VEPCD model, two types of laboratory tests were performed: 1) a constant crosshead strain rate tension test, 2) a fatigue test with randomly selected load levels and frequencies.

• Journal of the Earthquake Engineering Society of Korea
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• v.8 no.5 s.39
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• pp.35-43
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• 2004

Small-scale models have been frequently used for seismic performance tests because of limited testing facilities and economic reasons. However, there are not enough studies on similitude law for analogizing prototype structures accurately with small-scale models, although conventional similitude law based on geometry is not well consistent in the inelastic seismic behavior. When fabricating prototype and small-scale model of reinforced concrete structures by using the same material, added mass is demanded from a volumetric change and scale factor could be limited due to aggregate size. Therefore, it is desirable that different material is used for small-scale models. Thus, a modified similitude law could be derived depending on geometric scale factor, equivalent modulus ratio and ultimate strain ratio. In this study, compressive strength tests are conducted to analyze the equivalent modulus ratio of micro-concrete to normal-concrete. Then, equivalent modulus ratios are divided into multi-phase damage levels, which are basically dependent on ultimate strain level. Therefore, an algorithm adaptable to the pseudodynamic test, considering equivalent multi-phase similitude law based on seismic damage levels, is developed. Test specimens, consisted of prototype structures and 1/5 scaled models as a reinforced concrete column, were designed and fabricated based on the equivalent modulus ratios already defined. Finally quasistatic and pseudodynamic tests on the specimens are carried out using constant and variable modulus ratios, and correlation between prototype and small-scale model is investigated based on their test results. It is confirmed that the equivalent multi-phase similitude law proposed in this study could be suitable for seismic performance tests on small-scale models.

• Journal of the Korean GEO-environmental Society
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• v.16 no.12
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• pp.23-35
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• 2015

Nonlinear soil behavior before failure under dynamic loading is often implemented in a numerical analysis code by a mathematical fitting function model with Masing's rule. However, the model may show different behavior with an experimental results obtained from laboratory test in damping ratio corresponding secant shear modulus for a certain shear strain rage. The difference may come from an unique soil characteristics which is unable to implement by using the existing mathematical fitting model. As of now, several fitting models have been suggested to overcome the difference between model and real soil behavior but consequence of the difference in dynamic analysis is not reviewed yet. In this paper, the effect of the difference on site response was examined through nonlinear response history analysis. The analysis was verified and calibrated with well defined dynamic geotechnical centrifuge test. Site response analyses were performed with three mathematical fitting function models and compared with the centrifuge test results in prototype scale. The errors on peak ground acceleration between analysis and experiment getting increased as increasing the intensity of the input motion. In practical point of view, the analysis results of accuracy with the fitting model is not significant in low to mid input motion intensity.