• Journal of the Korean Statistical Society
• /
• v.9 no.1
• /
• pp.97-108
• /
• 1980

Variance components model appears often in designing experiments including time series data analysis. This paper is investigating the properties of the various procedures in estimating variance components for the two-way random model without interaction under normality. In this age of computer-oriented computations, MINQUE is found to be quite practicla because of the robustness with respect to the design configurations and parameters. Also adjusted AOV type estimation procedure is found to yield superior results over the unadjusted one.

• Journal of the Korean Statistical Society
• /
• v.22 no.1
• /
• pp.39-54
• /
• 1993

In the three-factor nested variance component model with equal numbers in the cells given by $y_{ijkm} = \mu + A_i + B_{ij} + C_{ijk} + \varepsilon_{ijkm}$, the exact confidence intervals of the variance component of $\sigma^2_A, \sigma^2_B, \sigma^2_C, \sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_{\varepsilon}, \sigma^2_B/\sigma^2_{\varepsilon}, \sigma^2_C/\sigma^2_{\varepsilon}, \sigma^2_A/\sigma^2_C, \sigma^2_B/\sigma^2_C$ and $\sigma^2_A/\sigma^2_B$ are not found out yet. In this paper approximate lower and upper confidence intervals are presented.

• Journal of the Korean Statistical Society
• /
• v.19 no.2
• /
• pp.139-144
• /
• 1990

For the first-order bilinear time series model $X_t = aX_{t-1} + e_i + be_{t-1}X_{t-1}$ where ${e_i}$ is a sequence of independent normal random variables with mean 0 and variance $\sigma^2$, the asymptotic distribution of sample autocarrelation function is obtained and shown to follow a normal distribution. The variance of the asymptotic distribution is of a complicated form and hence a bootstrap estimate of the variance is proposed for large sample inference. This result can be used to distinguish between different bilinear models.

• Communications for Statistical Applications and Methods
• /
• v.3 no.2
• /
• pp.29-36
• /
• 1996

In regression model with nested error structure interval estimations about variability on different stages are proposed. This article derives an approximate confidence interval on the variance in the first stage and an exact confidence interval on the variance in the second stage in two stage regression model. The approximate confidence interval is vased on Ting et al. (1990) method. Computer simulation is procided to show that the approximate confidence interval maintains the stated confidence coeffient.

• Journal of the Korean Statistical Society
• /
• v.32 no.2
• /
• pp.103-119
• /
• 2003

Properties of stochastic regression imputation are discussed under the uniform within-cell response model. Variance estimator is proposed and its asymptotic properties are discussed. A limited simulation is also presented.

• Journal of the Korean Statistical Society
• /
• v.23 no.1
• /
• pp.33-38
• /
• 1994

The OLS-estimator of the distribance variance in the linear regression model is shown to be asymptotically unbiased when the disturbances are MA(1)-process or particular s-th autocorrelated AR(s)-process.

• Journal of the Korean Statistical Society
• /
• v.26 no.3
• /
• pp.383-395
• /
• 1997

This paper proves that the standard bootstrap approximation for the least absolute deviation (LAD) estimate of .beta. in AR(1) processes with infinite variance error terms is asymptotically valid in probability when the bootstrap resample size is much smaller than the original sample size. The theoretical validity results are supported by simulation studies.

• Communications for Statistical Applications and Methods
• /
• v.2 no.2
• /
• pp.129-134
• /
• 1995

The ordinary least square estimator of the disturbance variance in the pooled cross-sectional and time series regression model is shown to be asymptotically unbiased without any restrictions on the regressor matrix when the disturbances follow an equicorrelated error component models.

• Journal of the Korean Statistical Society
• /
• v.5 no.1
• /
• pp.29-34
• /
• 1976

The techniques about the analysis of variance for quantitative variables have been well-developed. But when the variable is categorical, we must switch to a completely different set of varied techniques. R.J. Light and B.H. Margolin presented one kind of techniques for categorical data in their paper, where there are G unordered experimental groups and I unordered response categories.

• Journal of the Korean Statistical Society
• /
• v.4 no.2
• /
• pp.139-151
• /
• 1975

Individual effect of genes controlling quantitative traits can not ordinarily be distinguised from one another. Consequently, it is not possible to determine the mode of inheritance for single genes. By studying their combined effectsin segregating generations, however, one can gain some insight into their behavior and can make statistical inferences about their average gene action. The investigation reported herein was to extend genetic variance components and variance and covariance analyses, special attention was given to the genetic statistics from which least square estimators of genetic parameters are obtained.