• Tunnel and Underground Space
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• v.13 no.5
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• pp.344-352
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• 2003

The procedures to correct sampling biases for discontinuity data obtained from 1D survey line(borehole or scanline) is addressed. The Probability of intersection between the survey line and a circular discontinuity is considered, and a correction far orientation bias is developed assuming discontinuities as equivalent circular disks. The correction incorporates the effect of the angle between the direction of survey line and each discontinuity plane belonging to the discontinuity cluster, size of each discontinuity and length of the survey line. A procedure is provided to estimate unbiased discontinuity spacing parameters using the discontinuity spacing data based on the measurements carried out on a finite length of the survey line.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.461-475
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• 1998

In panel survey in which the sample is selected by stratified random sampling, if the sampling units shift from a stratum to others in time, then the movement should be incorporated in the estimation procedures. Dealing with the problem caused by the movement of units across stratum in the updated stratification sample, the bias of the conventional estimator neglecting the movement is investigated, arid the bias-adjusted estimators are proposed. The variance estimator of the suggested estimators is also derived. It is illustrated via a simulation study that the proposed estimators beat the conventional estimator in the sense of bias and mean squared error In particular, when the Neyman allocation is applied in stratified sampling, the proposed estimator is shown much more effective to this end.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.15-27
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• 2012

We have studied the realized variance(RV) of intra-day returns and market microstructure noise based on high-frequency stock transaction data for the four largest companies in terms of market capitalization in the KOSPI. First, non-negligible biases are observed for the RV and for the bias-corrected realized variance($RV_{AC_1}$) which is constructed by adjusting RV for the first order autocorrelation in intra-day returns. Bias is more obvious for the RV and the $RV_{AC_1}$ when intra-day returns are sampled more frequently than every 2 minutes. Transaction Time Sampling(TTS) is shown to be better than Calendar Time Sampling(CTS) in terms of biases of the RV and the $RV_{AC_1}$ for the 4 companies. The analysis reveals that market microstructure noise is temporally dependent. Second, by using the Noise-to-Signal Ratio(NSR), we estimate sampling frequencies that are optimal in terms of the Mean Square Errors(MSE) of the RV and the $RV_{AC_1}$. The optimal sampling frequencies are around 200 for RV and is around 5000 for the $RV_{AC_1}$ for all the four stock prices. For the 6 hour transaction period of the Korean stock trading, these correspond to about 2 minutes and 6 seconds.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.255-264
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• 2015

This paper addressed the problem of estimation of finite population mean in double sampling for stratification. This paper proposed a generalized ratio-cum-product type estimator of population mean. The bias and mean square error of the proposed estimator has been obtained upto the first degree of approximation. A particular member of the proposed generalized estimator was identified and studied from a comparison point of view. It is observed that the identified particular estimator is more efficient than usual unbiased estimator and Ige and Tripathi (1987) estimators. An empirical study was conducted in support of the theoretical findings.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.111-118
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• 2011

This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.103-129
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• 2019

The main goal of a case-control study is to learn the association between various risk factors and a primary outcome (e.g., disease status). Particularly recently, it is also quite common to perform secondary analyses of the case-control data in order to understand certain associations between the risk factors of the primary outcome. It has been repeatedly documented with case-control data, association studies of the risk factors that ignore the case-control sampling scheme can produce highly biased estimates of the population effects. In this article, we review the issues of the naive secondary analyses that do not account for the biased sampling scheme, and also the various methods that have been proposed to account for the case-control ascertainment. We additionally compare the results of many of the discussed methods in an example examining the association of a particular genetic variant with smoking behavior, where the data were obtained from a lung cancer case-control study.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.345-353
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• 2007

A Bayesian estimation of the Nakagami-m fading parameter is developed. Bayesian estimation is performed by Gibbs sampling, including adaptive rejection sampling. A Monte Carlo study shows that the Bayesian estimators proposed outperform any other estimators reported elsewhere in the sense of bias, variance, and root mean squared error.

• Journal of Integrative Natural Science
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• v.5 no.4
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• pp.241-245
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• 2012

Categorical data collected based on complex sample design is not proper for the standard Pearson multinomial-based chi-squared test because the observations are not independent and identically distributed. This study investigates effects of bias of point estimator of population proportion and its variance estimator to the standard Pearson chi-squared test statistics when the sample is collected based on complex sampling scheme. This study examines the effect under two population homogeneity test. The standard Pearson test statistic can be partitioned into two parts; the first part is the weighted sum of ${\chi}^2_1$ with eigenvalues of design matrix as their weights, and the additional second part which is added due to the biases of the point estimator and its variance estimator. Our empirical analysis shows that even though the bias of point estimator is small, Pearson test statistic is very much inflated due to underestimate the variance of point estimator. In the connection of design-based variance estimator and its design matrix, the bigger the average of eigenvalues of design matrix is, the larger relative size of which the first component part to Pearson test statistic is taking.

• Survey Research
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• v.10 no.1
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• pp.33-56
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• 2009

Landline telephone surveys of the population of households or individuals in Korea often use telephone directories as sampling frames. Recently, the frequency of unlisted numbers in the directories has been increased and the number of households without landline phones has become larger with a spread of mobile phones. Landline telephone coverage has currently reached to a level that raises concerns about the currently due to a coverage bias on the statistics in question. In this paper, we first present the distribution of telephone ownership in Korea and make a comparison with some selected countries. Second, we describe the characteristics of telephone directories. Next, we directly or indirectly estimate the telephone coverage rates of the frames, and show that it may nationally be lower than 65.6% based on additional information. We conclude with remarks about future studies to reduce coverage bias, including the developments of efficient random digit dialing sampling methods.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.485-499
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• 2022

Controlling the total survey error including sampling error and non-sampling error is very important in sampling design. Non-sampling error caused by non-response accounts for a large proportion of the total survey error. Many studies have been conducted to handle non-response properly. Recently, a lot of non-response imputation methods using machine learning technique and traditional statistical methods have been studied and practically used. Most imputation methods assume MCAR(missing completely at random) or MAR(missing at random) and few studies have been conducted focusing on MNAR (missing not at random) or NN(non-ignorable non-response) which cause bias and reduce the accuracy of imputation. In this study, we propose a non-response imputation method that can be applied to non-ignorable non-response. That is, we propose an imputation method to improve the accuracy of estimation by removing the bias caused by NN. In addition, the superiority of the proposed method is confirmed through small simulation studies.