• Journal of Animal Science and Technology
• /
• v.46 no.2
• /
• pp.137-144
• /
• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.183-194
• /
• 2004

본 연구에서는 공간 검색 통계량(spatial scan statistics)과 에셜론 해석법을 이용한 범주형 자료분석을 다룬다. 이를 위해 우선, 에셜론 덴드로그램을 이용하여 주어진 분활표의 계층적 구조(hierarchical structure)를 결정하고서 이로부터 핫스팟(hotspot)의 후보를 검출한다. 다음으로 우도비(likelihood ratio)를 기초로 유의하게 높거나 낮게 나타나는 지역에 대한 공간 검색 통계량을 산출한다. 마지막으로, 이 통계량을 바탕으로 핫스팟을 검출한다.

• The Korean Journal of Applied Statistics
• /
• v.18 no.1
• /
• pp.173-182
• /
• 2005

A Bayesian threshold model is considered to analyze binary or ordered categorical traits. Gibbs sampler for making full Bayesian inferences about the category probability as well as the regression coefficients is described. The model can be regarded as an alternative to the ordered logit regression model. Numerical examples are shown to demonstrate the efficiency of the model.

• The Korean Journal of Applied Statistics
• /
• v.27 no.4
• /
• pp.577-588
• /
• 2014

The collection and storage of a large volumes of data are becoming easier; however, the number of variables is sometimes more than the number of samples(objects). We now face the problem of dependency among variables(such as multicollinearity) due to the increased number of variables. We cannot apply various statistical methods without satisfying independency assumption. In order to overcome such a drawback we consider a categorizing (or discretizing) observations. We have a data set of nancial indices from banks in Korea that contain 78 variables from 16 banks. Genetic sequence data is also a good example of a large data and there have been numerous statistical methods to handle it. We discover lots of useful bank information after we transform bank data into categorical data that resembles genetic sequence data and apply bioinformatic techniques.

• Communications for Statistical Applications and Methods
• /
• v.4 no.2
• /
• pp.377-384
• /
• 1997

본 논문은 지분구조를 갖는 범주형 자료가 명목상의 다가자료일 때, 지분구조의 각 단계에서 정의될 수 있는 지분변수들의 유형과 지분변수들의 관심확률들에 영향을 미치는 변수들을 고려한 자료분석 모형들을 제시하고 있다.

• The Korean Journal of Applied Statistics
• /
• v.14 no.1
• /
• pp.39-57
• /
• 2001

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.

• Communications for Statistical Applications and Methods
• /
• v.4 no.3
• /
• pp.725-734
• /
• 1997

많은 사회과학 조사에서 분할표 형태로 얻어진 범주형 자료에는 오분류(misclassification)로 인한 오차가 내재되는 경우가 종종 있다. 질적속성 추정을 위한 확률화응답은 이러한 오분류 문제의 한 특수한 경우로 여겨지기도 한다. 그래서 확률화응답을 통한 범주형자료는 혼합된 분할표(mixed-up contingency table)로 여길 수 있는 바, 본 논문에서는 이에 대해 대수선형모형(log-linear model)을 설정하고 Chen과 Fienberg(1976)의 Iterative scaling procedure(ISP)에 의하여 얻어진 최우추정량의 극한을 이용하였다. 이 결과 Warner(1965) 형태의 대칭기법에 대해서는 Singh(1976)에 의하여 제안된 최우추정량과 같아지게 됨을 보임으로써 Warner에 의해서 제시된 추정량이 최우추정량으로 적절하지 않음을 확인해 보고, 무관질문기법에 대해서는 Greenberg, et al.(1969)에 의해서 제안된 추정량이 추정의 관점에서 최우추정량으로 적절하지 않음을 알아 보았다.

• The Korean Journal of Applied Statistics
• /
• v.11 no.1
• /
• pp.83-95
• /
• 1998

Statistical models are proposed for analyzing categorical data in the presence of missing observations or nonresponses which might occur in the sampling surveys and polls. As an illustration, we analyzed real polling data of the pre-presidential election in the USA, 1948, It had been predicted that Dewey would win the election. However, Truman won in the actual election.

• The Korean Journal of Applied Statistics
• /
• v.12 no.2
• /
• pp.433-447
• /
• 1999

검사관련 능력과 문항점수사이의 관계를 모형화하기 위해 사용한 순환모형에서 관측불능인 능력상대변수를 비롯한 모든 변수들이 범주형 변수라 가정하자. 이 범주형 자료를 위한 모수추정문제를 다루기 위해 EM 방법을 이용했는데, EM 방법은 사용하기에 편리하지만 순환모형에 대한 추정값이 적절하지 않는 경우가 발생한다. 그 주된 원인중의 하나로 초기값 선정의 잘못을 들 수 있는데, 본 논문에서는 이 외에 구조상의 결함도 그 원인이 됨을 경험적으로 보았다. 따라서 구조적 결함을 먼저 해결하면 보다 효과적인 초기값을 선정할 수 있으리가 기대한다.

• The Korean Journal of Applied Statistics
• /
• v.15 no.2
• /
• pp.381-393
• /
• 2002

K-modes algorithm and ROCK(RObust Clustering using linKs) algorithm we useful clustering methods for large categorical data. In the paper, we investigate these algorithms and propose improved algorithms of them to correct their weakness. A simulation study shows that the proposed algorithms could increase the performance of data clustering.