• Title, Summary, Keyword: 일반화추정방정식모형

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수정된 FS방법을 이용한 일반화된 지수생존모형의 추정

  • 하일도;조건호
    • Proceedings of the Korea Society for Industrial Systems Conference
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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.

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Generalized kernel estimating equation for panel estimation of small area unemployment rates (소지역 실업률의 패널추정을 위한 일반화커널추정방정식)

  • Shim, Jooyong;Kim, Youngwon;Hwang, Changha
    • 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.

Small Area Estimation via Generalized Estimating Equations and the Panel Analysis of Unemployment Rates (일반화추정방정식을 활용한 소지역 추정과 실업률패널분석)

  • Yeo, In-Kwon;Son, Kyoung-Jin;Kim, Young-Won
    • 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.

Bootstrap Estimation for GEE Models (일반화추정방정식(GEE)에 대한 부스트랩의 적용)

  • Park, Chong-Sun;Jeon, Yong-Moon
    • 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.

Small Sample Characteristics of Generalized Estimating Equations for Categorical Repeated Measurements (범주형 반복측정자료를 위한 일반화 추정방정식의 소표본 특성)

  • 김동욱;김재직
    • 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.

Comparison of Regression Model Approaches fined to Complex Survey Data (복합표본조사 데이터 분석을 위한 회귀모형 접근법의 비교: 소규모사업체조사 데이터 분석을 중심으로)

  • 이기재
    • 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.

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Generalized Exponential Regression Model with Randomly Censored Data (임의중도절단자료를 갖는 일반화된 지수회귀모형)

  • 하일도
    • Journal of the Korea Industrial Information Systems Research
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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.

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자본자산가격의 운동법칙을 표상하는 연속시간 확률매분방정식의 추정방법 - 비시뮬레이션 방법 -

  • Lee, Il-Gyun
    • The Korean Journal of Financial Studies
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    • v.10 no.1
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    • pp.1-44
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    • 2004
  • 연속시간모형은 시간의 흐름에 대응되는 자본자산의 운동의 성질과 시간의 흐름에 따라 형성되는 자본자산의 가격을 동시적으로 파악할 수 있는 것이 큰 장점이다. 연속시간 확률미분방정식을 구성하는 표류함수와 확산함수가 폐형해나 해석적 형태로 존재하지 않는 경우가 대부분이다. 여기에서 모수추정의 어려움이 발생한다. 전이 확률밀도함수의 인지 또는 발견의 어려움과 표류함수와 확산함수의 적분 불가능성은 최대가능도법의 사용을 어렵게 만든다. 여기에서 모수방법 보다는 비모수방법을 통하여 연속 확률 미분방정식을 추정하려는 성향이 존재한다. 밀도를 모르면 표본적률을 사용하여 모수를 추정할 수 있으므로 일반화 적률법이 연속시간 확률미분방정식의 모수 추정과 검정에 사용되고 있다. 전이밀도의 값을 시뮬레이션을 통하여 얻는 마코브연쇄 몬테카를로 방법, 전이밀도를 무한소 생성작용소를 통하여 얻는 방법, 비 모수방법, 여러 종류의 전개에 의하여 얻은 표류함수와 확산함수의 전이밀도에 대한 최대가능도법 등 여러 종류의 연속시간 확률미분방정식의 실증분석에서 사용되고 있다. 이 논문에서는 연속시간 확률미분방정식의 실증분석 방법들을 정리하는데 목적이 있다. 이일균(2004)은 이 논문과의 자매논문으로 시뮬레이션에 의한 확률미분방정식의 추정을 다루고 있어 시뮬레이션방법은 그 논문에 미룬다.

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Trend of Unmet Medical Need and Related Factors Using Panel Data (패널 자료를 이용한 미충족 의료의 추세와 관련요인)

  • Kim, Eun-Su;Eun, Sang-Jun
    • 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.

G-Inverse and SAS IML for Parameter Estimation in General Linear Model (선형 모형에서 모수 추정을 위한 일반화 역행렬 및 SAS IML 이론에 관한 연구)

  • Choi, Kuey-Chung;Kang, Kwan-Joong;Park, Byung-Jun
    • The Korean Journal of Applied Statistics
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    • v.20 no.2
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    • pp.373-385
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    • 2007
  • The solution of the normal equation arising in a general linear model by the least square methods is not unique in general. Conventionally, SAS IML and G-inverse matrices are considered for such problems. In this paper, we provide a systematic solution procedures for SAS IML.