Estimation of the exponential distribution based on multiply Type I hybrid censored sample

이경준,선호근,조영석 한국데이터정보과학회 2014 한국데이터정보과학회지 Vol.25 No.3

The exponential distibution is one of the most popular distributions in analyzing the lifetime data. In this paper, we propose multiply Type I hybrid censoring. And this paper presents the statistical inference on the scale parameter for the exponential distribution when samples are multiply Type I hybrid censoring. The scale parameter is estimated by approximate maximum likelihood estimation methods using two different Taylor series expansion types (AMLEI, AMLEII). We also obtain the maximum likelihood estimator (MLE) of the scale parameter sigma under the proposed multiply Type I hybrid censored samples. We compare the estimators in the sense of the root mean square error (RMSE). The simulation procedure is repeated 10,000 times for the sample size n=20 and 40 and various censored schemes. The AMLEII is better than AMLEI in the sense of the RMSE.

Exact likelihood inference of the exponential parameter under generalized Type II progressive hybrid censoring

이경준,선호근,조영석 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.1

Type II progressive hybrid censoring schemes (PHCS) have become quite popular in reliability and lifetime-testing studies. However, the drawback of the Type II PHCS is that it might take a very long time in order to complete the life test. In this article, we propose generalized Type II PHCS which provide a guarantee of the time to complete the life test. Also, we consider both exact and approximate inferential procedures based on generalized Type II PHCS when the lifetime distribution of the test items follows an independent and identically distributed (iid) exponential distribution. Given by the exact conditional first moment, bias adjusted MLE is proposed and its distribution is discussed. The results of simulation studies and real data analysis were conducted to evaluate the performance of the proposed method.

그룹 구조를 갖는 고차원 유전체 자료 분석을 위한 네트워크 기반의 규제화 방법

김기풍,최지윤,선호근,Kim, Kipoong,Choi, Jiyun,Sun, Hokeun 한국통계학회 2016 응용통계연구 Vol.29 No.6

고차원 유전체 자료를 사용하는 유전체 연관 분석에서는 벌점 우도함수 기반의 회귀계수 규제화 방법이 질병 및 표현형질에 영향을 주는 유전자를 발견하는데 많이 이용된다. 특히, 네트워크 기반의 규제화 방법은 유전체 연관성 연구에서의 유전체 경로나 신호 전달 경로와 같은 생물학적 네트워크 정보를 사용할 수 있으므로, Lasso나 Elastic-net과 같은 다른 규제화 방법들과 비교했을 경우 네트워크 기반의 규제화 방법이 보다 더 정확하게 관련 유전자들을 찾아낼 수 있다는 장점을 가지고 있다. 그러나 네트워크 기반의 규제화 방법은 그룹 구조를 갖고 있는 고차원 유전체 자료에는 적용시킬 수 없다는 문제점을 가지고 있다. 실제 SNP 데이터와 DNA 메틸화 데이터처럼 대다수의 고차원 유전체 자료는 그룹 구조를 가지고 있으므로 본 논문에서는 이러한 그룹 구조를 가지고 있는 고차원 유전체 자료를 분석하고자 네트워크 기반의 규제화 방법에 주성분 분석(principal component analysis; PCA)과 부분 최소 자승법(partial least square; PLS)과 같은 차원 축소 방법을 결합시키는 새로운 분석 방법을 제안하고자 한다. 새롭게 제안한 분석 방법은 몇 가지의 모의실험을 통해 변수 선택의 우수성을 입증하였으며, 또한 152명의 정상인들과 123명의 난소암 환자들로 구성된 고차원 DNA 메틸화 자료 분석에도 사용하였다. DNA 메틸화 자료는 대략 20,000여개의 CpG sites가 12,770개의 유전자에 포함되어 있는 그룹 구조를 가지고 있으며 Illumina Innium uman Methylation27 BeadChip으로부터 생성되었다. 분석 결과 우리는 실제로 암에 연관된 몇 가지의 유전자를 발견할 수 있었다. In genetic association studies with high-dimensional genomic data, regularization procedures based on penalized likelihood are often applied to identify genes or genetic regions associated with diseases or traits. A network-based regularization procedure can utilize biological network information (such as genetic pathways and signaling pathways in genetic association studies) with an outstanding selection performance over other regularization procedures such as lasso and elastic-net. However, network-based regularization has a limitation because cannot be applied to high-dimension genomic data with a group structure. In this article, we propose to combine data dimension reduction techniques such as principal component analysis and a partial least square into network-based regularization for the analysis of high-dimensional genomic data with a group structure. The selection performance of the proposed method was evaluated by extensive simulation studies. The proposed method was also applied to real DNA methylation data generated from Illumina Innium HumanMethylation27K BeadChip, where methylation beta values of around 20,000 CpG sites over 12,770 genes were compared between 123 ovarian cancer patients and 152 healthy controls. This analysis was also able to indicate a few cancer-related genes.

Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data

Hyoseok Ko,김기풍,선호근 한국유전체학회 2016 Genomics & informatics Vol.14 No.4

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling’s T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

Genome-Wide Association Study for Flowering Time in Korean Cowpea Germplasm

서은주,김기풍,Ryulyi Kang,김규태,박아론,김운지,선호근,하보근 한국육종학회 2020 Plant Breeding and Biotechnology Vol.8 No.4

Cowpea is an annual legume crop; although it is an essential food in developing countries, cowpea is now grownworldwide. For the genetic improvement of plants, flowering time is one of the major selection criteria. In general, flowering isregulated by photoperiod and temperature, along with the interaction between environmental factors. In this study, we aimed toinvestigate the candidate genes associated with flowering time using genome-wide association study (GWAS). To investigate theflowering time-related genes, 384 cowpea germplasms were genotyped with 51,128 single nucleotide polymorphisms (SNPs). Themain genetic component of days to flowering (DTF) was analyzed using genome association and prediction integrated tool (GAPIT)and elastic-net analyses. From the GAPIT and elastic-net analyses, a total of 23 SNPs were significantly associated with DTF amongfive (chr. 2, 3, 7, 9, and 11) and seven different chromosomes (chr. 1, 2, 3, 4, 5, 8, and 9), respectively. Based on our analysis,Vigun01g084000, Vigun01g227200, Vigun02g062600, and Vigun03g296800 were considered the major candidate genes that weresignificantly associated with DTF in cowpea. These results confirmed that DTF might be controlled by multiple genes affecting earlyflowering, delaying flowering time, repressing the transition to flowering, etc. This study will potentially contribute to effective DTFgenomic selection in plant breeding to better understand the genetic basis and explore the mechanism of flowering time.