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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Journal of Applied Statistics
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Journal DOI :
The Korean Statistical Society
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Volume & Issues
Volume 14, Issue 2 - Sep 2001
Volume 14, Issue 1 - Mar 2001
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A Comparison of PPS and Simple Cluster Sampling in Large Scale Sampling -Based on Economically Active Population Survey Sample Design
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 1~11
In PPS sampling, measure of size(MOS) is used to determine the probability of selection of sampling unit. However, some large scale surveys conducted in NSO(National Statistical Office) showed that the sampling units have the similar MOS. In such case, simple cluster sampling method instead of PPS sampling is recommended to give the interviewers a similar work load. In this paper, MSE and CV of the above two sampling methods applied to the 1997 Economically Active Population Survey sample design are compared.
Mean Estimation in Two-phase Sampling
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 13~24
In this paper, we investigated mean estimation methods in two-phase sampling. Under the fixed expected cost we reviewed the optimal sample sizes, minimum variances and approximate unbiased variance estimators for usual ratio estimator, stratified sample mean with proportional allocation and Rao's allocation of the second phase sample. Also we proposed combined ratio estimator, which uses both ratio estimation and stratification and derived optimal sample size, minimum variance and unbiased variance estimator. Through a limited simulation study, we compared estimators by design effects and came to know that ratio estimator is more efficient than stratified sample mean in some cases and inefficient in the other cases, but combined ratio estimator is more efficient than others in most cases.
The Marginal Model for Categorical Data Analysis of
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 25~37
The marginal model is proposed for the analysis of data which have c(2: 3) categories in the 3 x 3 cross-over trials with three periods and three treatments. This model could be used for the counterpart of the Kenward-Jones' joint probability one and should be the generalization of Balagtas et ai's univariate marginal logits one, which analyze the treatment effects in the 3 x 3 cross-over trials with binary response variables[Kenward and Jones(1991), Balagtas et al(1995)]. The model equations for the marginal probability are constructed by the three types of link functions. The methods would be given for making of the link function matrices and model ones, and the estimation of parameters shall be discussed. The proposed model is applied to the analysis of Kenward and Jones' data.
Empirical Bayesian Misclassification Analysis on Categorical Data
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 39~57
Categorical data has sometimes misclassification errors. If this data will be analyzed, then estimated cell probabilities could be biased and the standard Pearson X2 tests may have inflated true type I error rates. On the other hand, if we regard wellclassified data with misclassified one, then we might spend lots of cost and time on adjustment of misclassification. It is a necessary and important step to ask whether categorical data is misclassified before analyzing data. In this paper, when data is misclassified at one of two variables for two-dimensional contingency table and marginal sums of a well-classified variable are fixed. We explore to partition marginal sums into each cells via the concepts of Bound and Collapse of Sebastiani and Ramoni (1997). The double sampling scheme (Tenenbein 1970) is used to obtain informations of misclassification. We propose test statistics in order to solve misclassification problems and examine behaviors of the statistics by simulation studies.
Blocked Designs and Efficiency Factor Evaluation of Crosses Between Two Classes for Investigation of New Inbred Lines
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 59~70
Estimation of Asymmetric Bell Shaped Probability Curve using Logistic Regression
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 71~80
Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.
Generalized Composite Estimator with Intraclass Correlation in p-level Rotation Sampling
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 81~90
One of the Repeated survey which estimates variability of population, we can be consider rotation sample survey. There are two kinds of rotation sample survey - onelevel rotation sample survey and multi-level rotation sample survey. In rotation sample survey, Composite estimator is used to measure level or level change of the population. This study suggests Generalized Composite estimator as considering intraclass correlation in multi-level rotation sample survey, and optimal weight minimizing variance of estimator. Numerical example shows efficiency of Generalized Composite estimator as considering intraclass correlation according to the sample unit and change degree of intraclass correlation in the rotation group.
A Combined Multiple Regression Trees Predictor for Screening Large Chemical Databases
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 91~101
It has been shown that the multiple trees predictors are more accurate in reducing test set error than a single tree predictor. There are two ways of generating multiple trees. One is to generate modified training sets by resampling the original training set, and then construct trees. It is known that arcing algorithm is efficient. The other is to perturb randomly the working split at each node from a list of best splits, which is expected to generate reasonably good trees for the original training set. We propose a new combined multiple regression trees predictor which uses the latter multiple regression tree predictor as a predictor based on a modified training set at each stage of arcing. The efficiency of those prediction methods are compared by applying to high throughput screening of chemical compounds for biological effects.
A Sequence of Models for Categorical Data with Compound Scales
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 103~110
This paper considers a multistage experiment. Response scales can be same or different from stage to stage. When variables are of nested structure, the response variable at each stage can be defined conditionally. For analysing such data with compound scales, this paper suggests a sequnce of dependence models and shows how to set up a sequence of models for the driver's liscense test data.
A Study on Efficiency of the Cut-off Systematic Sampling
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 111~120
Either systematic sampling or stratified sampling is usually applied to the business conditions survey when companies don't have much difference in their size. But the cutoff systematic sampling is an efficient method when only a few companies are so large that the total of them almost equals to the total of whole companies. Throughout this paper, three estimators of total and their variance estimations depending on three kinds of sampling schemes are discussed, and are compared with them via their variances. It is proved that the cut-off systematic sampling is most efficient by using a real data of the logging business conditions survey.
A Comparison of Predictors in a Panel Data Regression Model
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 121~135
This paper derives the BLUP in a panel data regression model with two way error components and investigates the performance of various predictors. Through simulation study and real data anaysis some of basic finding is following: the computationally simple FGLS(AM, SA) predictors perform reasonably well when compared with the computationally involved MLE and RMLE predictors.
Principal Component Analysis of Compositional Data using Box-Cox Contrast Transformation
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 137~148
Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.
Two-sample Tests for Edge Detection in Noisy Images
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 149~160
In this paper we employ two-sample location tests such as Wilcoxon test and T test for detecting edges in noisy images. For this, we compute a test statistic on pixel gray levels obtained using an edge-height parameter and compare it with a threshold determined by a significance level. Experimental results applied to sample images are given and performances of these tests in terms of the objective measure are compared.
A Bayesian Method to Semiparametric Hierarchical Selection Models
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 161~175
Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. Hierarchical models including selection models are introduced and shown to be useful in such Bayesian meta-analysis. Semiparametric hierarchical models are proposed using the Dirichlet process prior. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierachical selection model with including unknown weight function and use Markov chain Monte Carlo methods to develop inference for the parameters of interest. Using Bayesian method, this model is used on a meta-analysis of twelve studies comparing the effectiveness of two different types of flouride, in preventing cavities. Clinical informative prior is assumed. Summaries and plots of model parameters are analyzed to address questions of interest.
Strategy for Visual Clustering
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 177~190
Monte Carlo Random Permutation Tests for Incompletely Ranked Data
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 191~199
A Note on Statistical Concepts Being Improperly Used in Sports
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 201~210
On Tests for Marginal Homogeneity
Korean Journal of Applied Statistics, volume 14, issue 1, 2001, Pages 211~221