Goodness-of-fit Tests based on Lorenz Curve for Progressive Censored Data from Normal Distribusion

• 발행기관: 한국자료분석학회
• 저자명: Hyein Koo Kyeongjun Lee
• 간행물 정보: 『한국자료분석학회 학술대회자료집』2021년 동계학술대회 발표집, 115~117쪽, 전체 3쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2022.01.27

영문 초록

The problem of examining how well a assumed distribution fits the data of a sample is of significant that has to be examined prior to any inferential process. In this paper, a new goodness-of-fit test for an normal distribution based on progressive censored data is proposed. Using Monte Carlo simulation studies, the present researchers have observed that the proposed test for normality is consistent and quite powerful in comparison with existing goodness-of-fit tests based on progressive censored data. Also, the new test statistic for a real data set is used and the results show that our new test statistic performs well.

목차

Ⅰ. Introduction

References

키워드

Goodness-of-fit test Lorenz curve Normal distribution order statistics progressive censoring