• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1059-1071
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• 2008

In a balanced factorial design with a nested factor where crossed factors as well as a nested factor exist simultaneously, powers of the rank transformed FR statistic for testing the main, nested and interaction effects are superior to those of the parametric F statistic. In heavy tailed distributions such as exponential and double exponential distributions, powers of the FR statistic show much higher level than those of the F statistic. Further powers of the F and FR statistic for testing the main effect show the highest level in an absolute size as compared with powers of the F and FR statistic for testing the nested and interaction effects. However powers of the FR statistic for testing the nested and interaction effects rather than the main effect are greater in a relative size than powers of F statistic for the all population distributions.

• Proceedings of the Safety Management and Science Conference
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• 2009.04a
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• pp.177-183
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• 2009

The research develops random ordering methodology of experimental design by the use of nested factor, block factor and repetition types. The spreadsheets developed are useful for quality practioner to acquire the experimental characteristics according to the random ordering.

• Proceedings of the Safety Management and Science Conference
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• 2011.04a
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• pp.695-699
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• 2011

The study presents an efficient design method of Venn Diagram that can be used when implementing the quality design of experiments based on generalizability theory. The paper examines four mixed and combined models that are designed by fixed factor, random factor, crossed factor and nested factor. The models considered in this research are $A^*{\times}B^*{\times}C$, (B: $A^*$)${\times}C$, $A{\times}B{\times}C$ and (B: A)${\times}C$.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1439-1448
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• 2013

The analysis of variance for randomized block design or two way classification data is well known. In this paper, particularly, we considered two stage nested design in which the levels of one factor is not identical for different levels of another factor. We investigate the structural properties of two stage nested design and the properties of error sum of squares for random effect model. For the application of two way nested design, we consider two-period crossover design which is used commonly for the equivalence test to bio-similar product. The confidence interval estimation of the difference of two population means in the crossover design is discussed based on statistical package SPSS.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.159-164
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• 1998

The two-factor nested variance component model with equal numbers in the cells are given by $y_{ijk}\;=\;{\mu}\;+\;A_i\;+\;B_{ij}\;+\;C_{ijk}$ and the confidence intervals for the ratio of variance components, ${\sigma}^{2}_{A}/{\sigma}^{2}_{B}$ are obtained in various forms by many authors. This article shows the probability ranges of these confidence intervals on ${\sigma}^{2}_{A}/{\sigma}^{2}_{B}$ proved by the mathematical computation.

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

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.181-188
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• 2009

This paper discusses the covariance structures of data collected from an experiment with a nested design structure, where a smaller experimental unit is nested within a larger one. Due to the nonrandomization of repeated measures factors to the nested experimental units, compound symmetry covariance structure is assumed for the analysis of data. Treatments are given as the combinations of the levels of random factors and fixed factors. So, a mixed-effects model is suggested under compound symmetry structure. An example is presented to illustrate the nesting in the experimental units and to show how to get the parameter estimates in the fitted model.

• East Asian mathematical journal
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• v.9 no.2
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• pp.333-345
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• 1993

• Journal of the Korea Safety Management & Science
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• v.12 no.1
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• pp.199-205
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• 2010

The research proposes the three-factor random measurement models for estimating the precision about operator, part, tool, and various measurement environments. The combined model with crossed and nested factors is developed to analyze the approximate F test by degrees of freedom given by Satterthwaite and point estimation of precisions from expected mean square. The model developed in this paper can be extended to the three useful models according to the type of nested designs. The study also provides the three-step procedures to evaluate the measurement precisions using three indexes such as SNR(Signal-To-Noise Ratio), R&R TR(Reproducibility&Repeatability-To-Total Precision Ratio), and PTR(Precision-To-Tolerance Ratio), The procedures include the identification of resolution, the improvement of R&R reduction, and the evaluation of precision effect.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.1035-1046
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• 2015

Variance components models incorporate various random factors in the form of linear models. There are two experimental Layouts for the classification of factors under variance components models: nested classification and crossed classification. We consider two-way variance components models and investigate the effect of experimental Layout on the performance of model selection criteria AIC and BIC. The effect of experimental Layout is studied through a simulation study with various combinations of parameters in a systematic fashion. The simulation study shows differences in performance of model selection methods between the two classification. There is a particular tendency to prefer the smaller model than the true model when the variance component of a nested factor becomes relatively larger than a nesting factor that is persistent even when the sample size is not small.