• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.481-493
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• 1995

An ARIMA signal observed with measurement error is shown to have another ARIMA representation with nonlinear restrictions on parameters. For this model, the restricted Newton-Raphson estimator(RNRE) of the unit root is shown to have the same limiting distribution as the ordinary least squares estimator of the unit root in an AR(1) model tabulated by Dickey and Fuller (1979). The RNRE of parameters of the ARIMA(p,1,k) process and unit root tests base on the RNRE are developed.

• Communications for Statistical Applications and Methods
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• 제20권1호
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• pp.85-96
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• 2013

A new bootstrap method combined with the stationary bootstrap of Politis and Romano (1994) and the classical residual-based bootstrap is applied to stationary autoregressive (AR) time series models. A stationary bootstrap procedure is implemented for the ordinary least squares estimator (OLSE), along with classical bootstrap residuals for estimated errors, and its large sample validity is proved. A finite sample study numerically compares the proposed bootstrap estimator with the estimator based on the classical residual-based bootstrapping. The study shows that the proposed bootstrapping is more effective in estimating the AR coefficients than the residual-based bootstrapping.

• Journal of Periodontal and Implant Science
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• 제45권1호
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• pp.2-7
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• 2015

A fundamental problem in analyzing complex multilevel-structured periodontal data is the violation of independency among the observations, which is an assumption in traditional statistical models (e.g., analysis of variance and ordinary least squares regression). In many cases, aggregation (i.e., mean or sum scores) has been employed to overcome this problem. However, the aggregation approach still exhibits certain limitations, such as a loss of power and detailed information, no cross-level relationship analysis, and the potential for creating an ecological fallacy. In order to handle multilevel-structured data appropriately, mixed effects models have been introduced and employed in dental research using periodontal data. The use of mixed effects models might account for the potential bias due to the violation of the independency assumption as well as provide accurate estimates.

• 대한가정학회지
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• 제33권3호
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• pp.291-298
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• 1995

본 연구는 주거소비규범에서의 이탈과 관계된 변수들을 조사하고자 시도되었으며, 이러한 목적을 달성하기 위한 구체적인 연구과제는 다음과 같다. 1. 미국의 각기 다른 가구유형별(가족생활주기 측면에서 구성된) 주거소비규범이 존재하는가\ulcorner 2. 규범보다 높은수준의 주거소비와 관계된 변수는 무엇인가\ulcorner 규범보다 낮은 수준의 주거소비와 관계된 변수는 무엇인가\ulcorner 자료는 미국엣 실시된 1990년도 소비자지출 조사의 인터뷰대상에서 추출된 4,923개의 소비자단위로 연구과제를 해결하기 위해 ordinary least squares(OLS) 중회귀분석방법이 사용되었으며, 본 연구에서 주거소비규범은 다음과 같은 4가지 주거특성, 즉 방수, 주거유형, 침실당 사람수 및 주거소유유형으로 확인되었다. 분석결과, 가구유형 및 크기가 규범이 존재한다고 믿어지는 4가지 주거특성과 유의하게 관련되어 있었으며, 이는 이러한 특성에 대해 규범이 존재한다는 결론을 지지했다. 한편, 다른 많은 가구들이 그들의 가구유형규범과 크기규범에 일치하지 않는 주거에서 살고 있는 것으로 나타났는데, 이러한 이탈은 소득, 가장의 연령 및 성별과 같이 자원제약을 나타내는 변수와 관계되어 있었다. 본 연구의 결과는 왜 인구통계적 변수가 주거 및 다른 소비와 관계 되는지에 대한 이유를 설명할 수 있도록 하는 근거를 제시하고 있으며, 주거소비만족과 주거조정행동의 근거를 이해하는데 적용될 수 있을 것이다.

• 대한전기학회논문지:전력기술부문A
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• 제52권8호
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• pp.450-456
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• 2003

This paper proposes a multiple time-series model with dummy variables for one-hour ahead load forecasting. We used 11 dummy variables that were classified by day characteristics such as day of the week, holiday, and special holiday. Also, model specification and selection of input variables including dummy variables were made by test statistics such as AIC(Akaike Information Criterion) and t-test statistics of each coefficient. OLS (Ordinary Least Squares) method was used for estimation and forecasting. We found out that model specifications for each hour are not identical usually at 30％ of optimal significance level, and dummy variables reduce the forecasting error if they are classified properly. The proposed model has much more accurate estimates in forecasting with less MAPE (Mean Absolute Percentage Error).

• 약학회지
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• 제56권1호
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• pp.66-69
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• 2012

This study aims to assess the aggregate contribution of new drugs to the increase in life expectancy. We constructed a panel data combining mortality data in KOSIS and a drug dataset generated by assigning new drugs listed in 2000~2009 to their respective ICD codes. We found that 10% increase in stock of new drug led to 0.13~0.27% increase in the probability of survival to age 65. Due to lack of disease-specific life table, we used indirect approach to estimate the effect of new drugs on longevity. Using ordinary least squares, the estimate of the probability of survival to age 65 (logarithm) on life expectancy for all ages was 24.92. In conclusion, the increase in life expectancy of the entire population in Korea between 2000 and 2009 resulting from NMEs is 1.95 years, which explains 46.6% of real increase in life expectancy.

• 아동학회지
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• 제27권3호
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• 기술혁신연구
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• 제12권3호
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• pp.97-109
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• 2004

According to "structure-conduct-performance" paradigm in IO, market structure (concentration) determines conduct (R&D investments), and conduct yields market performance (ratio of price to marginal cost). Previous empirical studies on Schumpeter Mark I, II assumed that the explanatory variable (market structure) and the disturbance are uncorrelated in the R&D equation. In this situation, Ordinary Least Squares (OLS) estimates of the structural parameters are inconsistent, because the endogeneous variables (R&D and market structure) can be determined simultaneously. So, in this study, full information (or system methods) estimation is used to test Schumpeter hypothesis since joint estimation can as well bring efficiency gains in the seemingly uncorrelated regressions (SUR) setting.

• Communications for Statistical Applications and Methods
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• 제29권5호
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• pp.615-627
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• 2022

Principal Fitted Component (PFC) is a semi-parametric sufficient dimension reduction (SDR) method, which is originally proposed in Cook (2007). According to Cook (2007), the PFC has a connection with other usual non-parametric SDR methods. The connection is limited to sliced inverse regression (Li, 1991) and ordinary least squares. Since there is no direct comparison between the two approaches in various forward regressions up to date, a practical guidance between the two approaches is necessary for usual statistical practitioners. To fill this practical necessity, in this paper, we newly derive a connection of the PFC to covariance methods (Yin and Cook, 2002), which is one of the most popular SDR methods. Also, intensive numerical studies have done closely to examine and compare the estimation performances of the semi- and non-parametric SDR methods for various forward regressions. The founding from the numerical studies are confirmed in a real data example.

• 수완나부미
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• 제15권2호
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• pp.215-244
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• 2023

This study analyzes the effect of macroeconomic indicators such as foreign direct investment (FDI), domestic investment, trade, inflation, unemployment, population, and governance indicators on economic growth and points out the GDP growth rate in 2002- 2019 among ASEAN countries. Data were compiled from the Worldwide Governance Indicators (WGI) and the World Bank, and the effect of variables on GDP was predicted using the pooled ordinary least squares (POLS), fixed effects model (FEM), and random effects model (REM) methods. As a measure of growth, the GDP growth rate has been taken; FDI and domestic investment, trade, inflation, and governance indicators are positively connected and have an influence on economic growth in these ASEAN countries; domestic investment, population, and unemployment have a negative relationship to economic growth. The macroeconomic indicators and institutional stability of the nation have an effect on its economic growth. Comprehensive institutional stability and well-laid macroeconomic policies are required for growth to materialize.