• Title/Summary/Keyword: Censoring

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Goodness of Fit Tests for the Exponential Distribution based on Multiply Progressive Censored Data (다중 점진적 중도절단에서 지수분포의 적합도 검정)

  • Yun, Hyejeong;Lee, Kyeongjun
    • Journal of the Korean Data Analysis Society
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    • v.20 no.6
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    • pp.2813-2827
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    • 2018
  • Progressive censoring schemes have become quite popular in reliability study. Under progressive censored data, however, some units can be failed between two points of observation with exact times of failure of these units unobserved. For example, loss may arise in life-testing experiments when the failure times of some units were not observed due to mechanical or experimental difficulties. Therefore, multiply progressive censoring scheme was introduced. So, we derives a maximum likelihood estimator of the parameter of exponential distribution. And we introduced the goodness-of-fit test statistics using order statistic and Lorenz curve. We carried out Monte Carlo simulation to compare the proposed test statistics. In addition, real data set have been analysed. In Weibull and chi-squared distributions, the test statistics using Lorenz curve are more powerful than test statistics using order statistics.

Estimating survival distributions for two-stage adaptive treatment strategies: A simulation study

  • Vilakati, Sifiso;Cortese, Giuliana;Dlamini, Thembelihle
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.411-424
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    • 2021
  • Inference following two-stage adaptive designs (also known as two-stage randomization designs) with survival endpoints usually focuses on estimating and comparing survival distributions for the different treatment strategies. The aim is to identify the treatment strategy(ies) that leads to better survival of the patients. The objectives of this study were to assess the performance three commonly cited methods for estimating survival distributions in two-stage randomization designs. We review three non-parametric methods for estimating survival distributions in two-stage adaptive designs and compare their performance using simulation studies. The simulation studies show that the method based on the marginal mean model is badly affected by high censoring rates and response rate. The other two methods which are natural extensions of the Nelson-Aalen estimator and the Kaplan-Meier estimator have similar performance. These two methods yield survival estimates which have less bias and more precise than the marginal mean model even in cases of small sample sizes. The weighted versions of the Nelson-Aalen and the Kaplan-Meier estimators are less affected by high censoring rates and low response rates. The bias of the method based on the marginal mean model increases rapidly with increase in censoring rate compared to the other two methods. We apply the three methods to a leukemia clinical trial dataset and also compare the results.

New Accelerated Life Test Plans for Weibull and Lognormal Lifetime Distributions (와이블과 대수정규 수명분포를 따를 때 새로운 가속수명시험 계획의 개발)

  • Seo, Sun-Keun
    • Journal of Applied Reliability
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    • v.14 no.3
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    • pp.182-190
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    • 2014
  • This paper presents new practical accelerated life test plans with different censoring times at three levels of stress for Weibull and lognormal lifetime distributions, respectively. The proposed plans are compared with the corresponding two-level statistically optimal plans and three-level compromise and practical plans. Computational results indicate that new practical plans have been more precise and effective than the existing three-level plans under a constraint of total testing time. In addition, a procedure to determine useful ALT plans is illustrated with a numerical example.

Intrinsic Bayes Factors for Exponential Model Comparison with Censored Data

  • Kim, Dal-Ho;Kang, Sang-Gil;Kim, Seong W.
    • Journal of the Korean Statistical Society
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    • v.29 no.1
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    • pp.123-135
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    • 2000
  • This paper addresses the Bayesian hypotheses testing for the comparison of exponential population under type II censoring. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic, expected and median intrinsic Bayes factors for our problem. The Monte Carlo simulation is used for calculating intrinsic Bayes factors which are compared with P-values of the classical test.

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Empirical Bayes Nonparametric Estimation with Beta Processes Based on Censored Observations

  • Hong, Jee-Chang;Kim, Yongdai;Inha Jung
    • Journal of the Korean Statistical Society
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    • v.30 no.3
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    • pp.481-498
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    • 2001
  • Empirical Bayes procedure of nonparametric estiamtion of cumulative hazard rates based on censored data is considered using the beta process priors of Hjort(1990). Beta process priors with unknown parameters are used for cumulative hazard rates. Empirical Bayes estimators are suggested and asymptotic optimality is proved. Our result generalizes that of Susarla and Van Ryzin(1978) in the sensor that (i) the cumulative hazard rate induced by a Dirichlet process is a beta process, (ii) our empirical Bayes estimator does not depend on the censoring distribution while that of Susarla and Van Ryzin(1978) does, (iii) a class of estimators of the hyperprameters is suggested in the prior distribution which is assumed known in advance in Susarla and Van Ryzin(1978). This extension makes the proposed empirical Bayes procedure more applicable to real dta sets.

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Confidence Interval for the Difference or Ratio of Two Median Failure Times from Clustered Survival Data

  • Lee, Seung-Yeoun;Jung, Sin-Ho
    • The Korean Journal of Applied Statistics
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    • v.22 no.2
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    • pp.355-364
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    • 2009
  • A simple method is proposed for constructing nonparametric confidence intervals for the difference or ratio of two median failure times. The method applies when clustered survival data with censoring is randomized either (I) under cluster randomization or (II) subunit randomization. This method is simple to calculate and is based on non-parametric density estimation. The proposed method is illustrated with the otology study data and HL-A antigen study data. Moreover, the simulation results are reported for practical sample sizes.

Adaptive Smoothing Algorithm Based on Censoring for Removing False Color Noise Caused by De-mosaicing on Bayer Pattern CFA (Bayer 패턴의 de-mosaicing 과정에서 발생하는 색상잡음 제거를 위한 검열기반 적응적 평탄화 기법)

  • Hwang, Sung-Hyun;Kim, Chae-Sung;Moon, Ji-He
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.403-406
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    • 2005
  • The purpose of this paper is to propose ways to remove false color noise (FCN) generated during de-mosaicing on RGB Bayer pattern images. In case of images sensors adapting Bayer pattern color filters array (CFA), de-mosaicing is conducted to recover the RGB color data in single pixels. Here, FCN phenomena would occur where there is clearer silhouette or contrast of colors. The FCN phenomena found during de-mosaicking process appears locally in the edges inside the image and the proposed method of eliminating this is to convert RGB color space to YCbCr space to conduct smoothing process. Moreover, for edges where different colors come together, censoring based smoothing technique is proposed as a way to minimize color blurring effect.

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