• Survey Research
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• v.2 no.1
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• pp.73-86
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• 2001

In this paper. we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. We compared the regression models using the design-based approach and the generalized estimating equations (GEEs) approach with the model-based approach through the design and weighting effects analysis.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.205-209
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• 1999

일반화된 지수생존모형(generalized exponential survival model)을 고려하여 이 모형의 모수를 추정하는 수정된 FS(modified Fisher scoring)방법을 제안한다. 이를 위해 우도방정식(likelihood equation)을 유도하고 초기추정치 (initial estimate)를 포함한 추정알고리즘(estimating algorithm)을 개발한다.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1199-1210
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• 2013

The high unemployment rate is one of the major problems in most countries nowadays. Hence, the demand for small area labor statistics has rapidly increased over the past few years. However, since sample surveys for producing official statistics are mainly designed for large areas, it is difficult to produce reliable statistics at the small area level due to small sample sizes. Most of existing studies about the small area estimation are related with the estimation of parameters based on cross-sectional data. By the way, since many official statistics are repeatedly collected at a regular interval of time, for instance, monthly, quarterly, or yearly, we need an alternative model which can handle this type of panel data. In this paper, we derive the generalized kernel estimating equation which can model time-dependency among response variables and handle repeated measurement or panel data. We compare the proposed estimating equation with the generalized linear model and the generalized estimating equation through simulation, and apply it to estimating the unemployment rates of 25 areas in Gyeongsangnam-do and Ulsan for 2005.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.665-674
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• 2008

Most of existing studies about the small area estimation deal with the estimation of parameters based on cross-sectional data. However, since many official statistics are repeatedly collected at a regular interval of time, for instance, monthly, quarterly, or yearly, we need an alternative model which can handle characteristics of these kinds of data. In this paper, we investigate the generalized estimating equation which can model time-dependency among response variables and is useful to analyze repeated measurement or longitudinal data. We compare with the generalized linear model and the generalized estimating equation through the estimation of unemployment rates of 25 areas in Gyeongsangnam-do and Ulsan. The data consist of the status of employment and some covariates from January to December 2005.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.207-216
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• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.

• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.297-310
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• 2002

Liang and Zeger proposed generalized estimating equations(GEE) for analyzing repeated data which is discrete or continuous. GEE model can be extended to model for repeated categorical data and its estimator has asymptotic multivariate normal distribution in large sample sizes. But GEE is based on large sample asymptotic theory. In this paper, we study the properties of GEE estimators for repeated ordinal data in small sample sizes. We generate ordinal repeated measurements for two groups using two methods. Through Monte Carlo simulation studies we investigate the empirical type 1 error rates, powers, relative efficiencies of the GEE estimators, the effect of unequal sample size of two groups, and the performance of variance estimators for polytomous ordinal response variables, especially in small sample sizes.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.73-86
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• 2001

In this paper, we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. We compared the regression models using the design-based approach and the generalized estimating equations(GEEs) approach with the model-based approach through the design and weighting effects analysis.

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.229-236
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• 2020

The purpose of this study was to investigate the current status of unmet medical need using data from the Korea Health Panel study from 2009 to 2013 (excluding 2010), and to analyze the trends of unmet medical need and related factors. The subjects of this study were 11,598 in 2009, 11,035 in 2011, 10,584 in 2012, 10,099 in 2013, and 7,144 people in panel data, and conducted frequency analysis, chi-square test and generalized estimating equation. As a result of the analysis by year, it was found that women, under middle school graduation, medical aid, the lowest household income and low subjective health status experienced more unmet medical need. As a result of analysis using generalized estimating equation, women, under 40 years of age, under elementary school graduation, lowest quartile household income, subjective health status of less than 20 points, and activity restrictions are more likely to experience unmet medical need. Based on these results, we intend to provide basic data for establishing policies on the use of medical services.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.39-43
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• 1999

We consider generalized exponential regression model with randomly censored data and propose a modified Fisher scoring method which estimates the model parameters. For this, the likelihood equations are derived and then the estimating algorithm is developed. We illustrate the proposed method using a real data.

• Journal of the Korean Statistical Society
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• v.6 no.2
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• pp.117-154
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• 1977

본논문에서는 첫째로 단일방정식 모형에서 종속변수가 양자택일(binary choice)의 이산확률변수일 때 이러한 이진적 종속변수(binary dependent variable)의 변동을 설명하는데 사용되는 몇 가지 모형을 소개하고 각각의 표기 및 추정방법, 추정량의 성질, 예측 및 검정 문제 등에 관하여 비교 서술하고자 한다. 둘째, 종속변수가 이산과 연속의 혼합형태일 때 앞에 소개된 모형이 어떻게 적용될 수 있는가를 살펴보며, 셋째, 선택대상 및 종속변수의 수가 증가하여 일반화된 선다형모형(multiple choice model)의 경우, 표기 및 추정방법을 단일방정식 기법을 이용하여 추가로 총람하고자 한다. 넷째, 본논문에서는 또한 내구소비재 구입에 관한 조사자료를 이용하여 실제 많이 사용되는 몇 개의 모형을 선택하여 적용하고 각각의 예측력을 분석함으로써 각 모형을 비교 검토하는데 목적이 있다.