• Proceedings of the Korean Association for Survey Research Conference
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• 2007.06a
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• pp.139-149
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• 2007

사회조사를 위한 표본설계를 할 때 표본의 크기를 얼마로 할 것인지를 결정하는 문제는 조사연구자에게 고민거리가 된다. 사회조사 중에서 4점 또는 5점 척도로 된 여러 개의 개별 문항들로 구성된 설문지를 사용하는 경우가 많다. 이런 경우 개개의 문항 자체를 직접적으로 하나의 변수로 사용하지 않고 여러 개 문항들을 결합하여 새로운 척도를 만들어 사용하는 것이 일반적이다. 본 연구의 목적은 리커트 척도가 관심변수인 조사연구에서 표본크기를 결정하는 방법을 제공하는 것이다. 리커트 척도를 만들고자 할 때 4점 혹은 5점 척도로 구성된 여러 문항변수들은 일반적으로 서로 양의 상관관계를 가지게 된다. 본 연구에서는 개별 문항변수들은 각각 동일한 분포를 가지며, 각각의 변수들은 서로 동일한 크기의 상관관계를 갖는다는 가정을 한다. 주어진 가정 하에서 새로운 척도의 표본분포를 유도한 후 이를 이용하여 다양한 상황에서의 표본의 크기를 계산한 결과를 표로 제시하게 되는데 표본이론을 잘 모르는 조사연구자들은 이 표를 이용하여 원하는 표본크기를 결정 할 수 있을 것이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.212-213
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• 2013

압축 센싱 기술은 성긴 (sparse)신호의 경우 Nyquist 표본화율보다 적은 수의 표본으로도 원신호를 완벽하게 복원할 수 있는 이론을 제시하고 있다. 전통적인 영상 처리분야에 압축 센싱 기술을 적용하는 연구를 시작함에 따라 계산 복잡도 및 메모리 문제로 블록 영상 기반 압축 센싱 방법을 많이 고려하고 있다. 또한, 이러한 압축 센싱 방법에서 복원 과정은 일정 허용 오차 범위 기준을 복원 신호가 만족시키는 경우에 종료되므로, 허용 오차 범위에 따른 복원 신호 품질과 계산 복잡도에 변화가 발생하게 된다. 본 논문에서는 블록 기반 압축 센싱 방법을 이용하여 영상을 복원함에 있어, 허용 오차 값에 따른 복원 영상의 화질 변화와 시간 절감 정도를 비교, 분석하였다.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.1077-1085
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• 2017

In this study, we have found the major factors which affect Korean women's wage analysing the data provided by 2015 Korea Labor Panel Survey (KLIPS). In general, wage data is difficult to analyze because random sampling is infeasible. Heckman sample selection model is the most widely used method for analysing the data with sample selection. Heckman proposed two kinds of selection models: the one is the model with maximum likelihood method and the other is the Heckman two stage model. Heckman two stage model is known to be robust to the normal assumption of bivariate error terms. Recently, Marchenko and Genton (2012) proposed the Heckman selectiont model which generalizes the Heckman two stage model and concluded that Heckman selection-t model is more robust to the error assumptions. Employing the two models, we carried out the analysis of the data and we compared those results.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.34-41
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.505-515
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• 1999

인공신경망 모형을 적합시키는데 사용하는 역전파 알고리즘을 로버스트하게 만드는 새로운 오차함수를 제안했으며, 새 방법의 성능을 확인하기 위해 Liano가 제안한 방법에 따라 모의실험을 수행했다. 실험결과 새 방법은 LMS방법만큼 안정적이었으며, Liano의 LMLS방법보다 더 로버스트했다. 또 실제 사례를 분석함으로써 이 방법이 의미있는 방법임을 보였다. 새 방법은 특히 오차가 없거나 작은 오차를 갖는 표본에 대해서도 좋은 성질을 가짐으로서 대형오차의 유무에 관계없이 항상 사용할 수 있는 방법으로 판명되었다.

• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.105-114
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• 2005

This paper describes a new measuring technique by analysing multiple time-series patterns. This paper's goal is that extracts a really measured value having a sample pattern which is the best matched with an inputted time-series, and calculates a difference ratio with the value. Therefore, the proposed technique is not a recognition but a measurement. and not a hardware but a software. The proposed technique is consisted of three stages, initialization, learning and measurement. In the initialization stage, it decides weights of all parameters using importance given by an operator. In the learning stage, it classifies sample patterns using LBG and DTW algorithm, and then creates code sequences for all the patterns. In the measurement stage, it creates a code sequence for an inputted time-series pattern, finds samples having the same code sequence by hashing, and then selects the best matched sample. Finally it outputs the really measured value with the sample and the difference ratio. For the purpose of performance evaluation, we tested on multiple time-series patterns obtained from etching machine which is a semiconductor manufacturing.

• Survey Research
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• v.11 no.1
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• pp.123-144
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• 2010

In new sample design for Korea Housing Survey to research about housing policy, total strata are forty five because individual results of sixteen regions are estimated. The sample size is determined by sample errors of several variables which are the living area, family income, householder income, and living expenses. The sample size of each region is determined by relative standard error of existing result, and the strata sample size is to use the square root proportion allocation. Enumeration districts are sampled by the probability proportion to size systematic sampling in proportion to the enumeration district size, and the systemic sampling to use assortment characteristics. We considered a new apartment complex because of variation reflections which are rebuilder and redevelopment of houses. To get estimators of mean and variance, we used the design weighting, non-response adjusting, and post-stratification. In order to consider estimation efficiency, we calculate the design effect using estimators of variance.

• 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.

• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.145-159
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• 1996

항목 무응답은 표본조사에서 비표본오차를 발생시키는 중요한 요인으로 지적되고 있다. 본 논문에서는 현재까지 통계조사의 분석과정에서 직관적으로 제시된 다양한 항목 무응답 대체방법들을 정리하고, 이런 방법들 간의 장.단점과 무응답의 발생 형태에 다른 대체 효과를 실제 사회조사 자료를 이용한 모의 실험을 통하여 비교, 분석하였다.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.501-512
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• 2014

Modified cut-off sampling is widely used for highly skewed data. A serious drawback of modified cut-off sampling is the difficulty of adjustment of non-response in take-all stratum. Therefore, solutions of the problems of non-response in take-all stratum have been studied in various ways such as substitute of samples, imputation or re-weight method. In this paper, a new cut-off point based on minimizing MSE being used in exponential and power functions is suggested and it can be reduced the number of take-all stratum. We also investigate another cut-off point determination method with underlying distributions such as truncated log-normal and truncated gamma distributions. Finally we suggest the optimal cut-off point which has a minimum of take-all stratum size among suggested methods. Simulation studies are performed and Labor Survey data and simulated data are used for the case study.