• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.515-525
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• 2010

We investigated design-based properties of the ordinary least square estimator(OLSE) and the weighted least square estimator(WLSE) in a panel regression model. Given a complex data we derive the magnitude of the design-based bias of two estimators and show that the bias of WLSE is smaller than that of OLSE. We also conducted a simulation study using Korean welfare panel data in order to compare design-based properties of two estimators numerically. In the study we found the followings. First, the relative bias of OLSE is nearly two times larger than that of WLSE and the bias ratio of OLSE is greater than that of WLSE. Also the relative bias of OLSE remains steady but that of WLSE becomes smaller as the sample size increases. Next, both the variance and mean square error(MSE) of two estimators decrease when the sample size increases. Also there is a tendency that the proportion of squared bias in MSE of OLSE increases as the sample size increase, but that of WLSE decreases. Finally, the variance of OLSE is smaller than that of WLSE in almost all cases and the MSE of OLSE is smaller in many cases. However, the number of cases of larger MSE of OLSE increases when the sample size increases.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.23-32
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• 2012

This paper deals with the bias and variance of regression coefficient estimators in a finite population. We derive approximate formulas for the bias, variance and mean square error of two estimators when we select a fixed-size inclusion probability proportional to the size sample and then estimate regression coefficients by the ordinary least square estimator as well as the weighted least square estimator based on the selected sample data. Necessary and sufficient conditions for the comparison of the two estimators in terms of variance and mean square error are suggested. In addition, a simple example is introduced to numerically compare the variance and mean square error of the two estimators.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.623-632
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• 1998

이원혼합모형에서 고정효과의 추정가능한 함수에 대한 신뢰구간을 구하는 경우에 어떤 분산성분추정량을 선택하는 것이 가장 바람직한가를 모의실험을 통하여 살펴본다 혼합모형에서는 t-분포와 일반화최소제곱추정량을 사용하여 신뢰구간을 구할 수 있는데, 일반적으로 분산성분을 알 수 없기 때문에 분산성분을 반드시 추정하여야만 한다. 이 경우 분산성분의 추정량으로 가장 많이 사용되는 추정량들인 Henderson의 방법 III 추정량, 사전추측값이 1인 MINQUE 추정량, MLE(최우추정량), REMLE(제한최우추정량)를 이용하여 분산행렬을 추정하고, 신뢰구간의 포함범위확률과 평균길이를 모의실험을 통하여 살펴본다. 모의실험의 결과는 4가지 추정량 모두 비슷한 신뢰구간의 포함범위확률과 평균길이를 갖는 것으로 판명되었다.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.157-169
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• 2016

We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

• The Korean Journal of Applied Statistics
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• v.12 no.1
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• pp.109-124
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• 1999

일반적으로 오차항이 자기상관되어 있는 선형회귀 모형에서는 회귀계수에 대한 보통최소제곱추정량이 효율적이지 못 하다고 알려져 있다. 그러나 이러한 일반화선형회귀모형에서 독립변수의 형태에 따라서는 OLSE의 사용 가능성을 제시하는 모형이 있다. 본 연구에서는 오차항이 일차 이동평균 과정을 따르는 선형회귀모형에서 여러 추정량들 (GLSE, APX, MAPX)에 대한 OLSE의 상대효율함수를 유도하고 비교 분석하고자 한다. 특히 소표본에서 정확한 상대효율값을 구하여 OLSE의 효율성이 크게 떨어지지 않거나 효율성이 나은 회귀모형들을 제시한다.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.1-11
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• 1995

The parameters in the multiplicative model $Y_{1}={\alpha}_{0}{\prod}^{p}_{k=1}X_{kj}^{{\beta}_K}v_{j}$ are usually estimated by the least squares method after logarithmic transformation, and the least square Estimator of ${\alpha}_{0}$ is known to be biased, i.e., $E(e xp(\hat{\beta}_{0})){\neq}{\alpha}_{0})$. In the present study the unbaised estimators of ${\alpha}_{0}$ are examined(1) by modifying the least squares estimator and (2) by applying the Finney's results. The variances are also compared. In addition it has been observed that multiplicative model can be used to express the relationship beetween rice yield and yield components.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.205-213
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• 2012

Transforming response variable is a general tool to adapt data to a linear regression model. However, it is well known that response transformations in linear regression are very sensitive to one or a few outliers. Many methods have been suggested to develop transformations that will not be influenced by potential outliers. Recently Cheng (2005) suggested to using a trimmed likelihood estimator based on the idea of the least trimmed squares estimator(LTS). However, the method requires presetting the number of outliers and needs many computations. A new method is proposed, that can solve the problems addressed and improve the robustness of the estimates. The method uses a stepwise procedure, suggested by Hadi and Simonoff (1993), to detect outliers that determine response transformations.