• Title/Summary/Keyword: left and right censored data

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Reliability analysis of warranty returns data (품질보증 반환 데이터의 신뢰성 분석)

  • Baik, Jaiwook;Jo, Jinnam
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.4
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    • pp.893-901
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    • 2014
  • A certain number of products are sold each month and some of them are returned for repair. In this study both return rate and cumulative return rate are shown on the graph to show the general trend of how many products are returned as time goes by. Next this type of summary data can be considered as a conglomeration of both left and right censored data. So reliability analysis is attempted for this type of summary data. Lastly, left censored data can be traced to find the exact time period during which the product has been claimed. In that case the left censored data can be taken as failure data. So similar type of reliability analysis is attempted for the resulting right censored data.

Robust Regression and Stratified Residuals for Left-Truncated and Right-Censored Data

  • Kim, Chul-Ki
    • Journal of the Korean Statistical Society
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    • v.26 no.3
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    • pp.333-354
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    • 1997
  • Computational algorithms to calculate M-estimators and rank estimators of regression parameters from left-truncated and right-censored data are developed herein. In the case of M-estimators, new statistical methods are also introduced to incorporate leverage assements and concomitant scale estimation in the presence of left truncation and right censoring on the observed response. Furthermore, graphical methods to examine the residuals from these data are presented. Two real data sets are used for illustration.

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Nonparametric Regression with Left-Truncated and Right-Censored Data

  • Park, Jinho
    • Communications for Statistical Applications and Methods
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    • v.6 no.3
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    • pp.791-800
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    • 1999
  • Gross and Lai(1996) proposed a new approach for ordinary regression with left-truncated and right-censored (I.t.r.c) data. This paper shows how to apply nonparametric algorithms such as multivariate adaptive regression splines to 1.t.r.c data.

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A General Mixed Linear Model with Left-Censored Data

  • Ha, Il-Do
    • Communications for Statistical Applications and Methods
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    • v.15 no.6
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    • pp.969-976
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    • 2008
  • Mixed linear models have been widely used in various correlated data including multivariate survival data. In this paper we extend hierarchical-likelihood(h-likelihood) approach for mixed linear models with right censored data to that for left censored data. We also allow a general random-effect structure and propose the estimation procedure. The proposed method is illustrated using a numerical data set and is also compared with marginal likelihood method.

Efficient Score Estimation and Adaptive Rank and M-estimators from Left-Truncated and Right-Censored Data

  • Chul-Ki Kim
    • Communications for Statistical Applications and Methods
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    • v.3 no.3
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    • pp.113-123
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    • 1996
  • Data-dependent (adaptive) choice of asymptotically efficient score functions for rank estimators and M-estimators of regression parameters in a linear regression model with left-truncated and right-censored data are developed herein. The locally adaptive smoothing techniques of Muller and Wang (1990) and Uzunogullari and Wang (1992) provide good estimates of the hazard function h and its derivative h' from left-truncated and right-censored data. However, since we need to estimate h'/h for the asymptotically optimal choice of score functions, the naive estimator, which is just a ratio of estimated h' and h, turns out to have a few drawbacks. An altermative method to overcome these shortcomings and also to speed up the algorithms is developed. In particular, we use a subroutine of the PPR (Projection Pursuit Regression) method coded by Friedman and Stuetzle (1981) to find the nonparametric derivative of log(h) for the problem of estimating h'/h.

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Nonpararmetric estimation for interval censored competing risk data

  • Kim, Yang-Jin;Kwon, Do young
    • Journal of the Korean Data and Information Science Society
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    • v.28 no.4
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    • pp.947-955
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    • 2017
  • A competing risk analysis has been applied when subjects experience more than one type of end points. Geskus (2011) showed three types of estimators of CIF are equivalent under left truncated and right censored data. We extend his approach to an interval censored competing risk data by using a modified risk set and evaluate their performance under several sample sizes. These estimators show very similar results. We also suggest a test statistic combining Sun's test for interval censored data and Gray's test for right censored data. The test sizes and powers are compared under several cases. As a real data application, the suggested method is applied a data where the feasibility of the vaccine to HIV was assessed in the injecting drug uses.

Estimation of the Mean and Variance for Normal Distributions whose Both Sides are Truncated

  • Hong, Chong-Sun;Choi, Yun-Young
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.249-259
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    • 2002
  • In order to estimate the mean and variance for a Normal distribution which is truncated at both right and left sides, maximum likelihood estimators based on the entire sample from the original distribution are compared with the sample mean and variance of the censored sample which is the data remaining after truncation using simulation. We found that, surprisingly, the mean squared error of the mean based on the censored data Is smaller than that of the full sample estimators.

CENSORED FUZZY REGRESSION MODEL

  • Choi, Seung-Hoe;Kim, Kyung-Joong
    • Journal of the Korean Mathematical Society
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    • v.43 no.3
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    • pp.623-634
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    • 2006
  • Various methods have been studied to construct a fuzzy regression model in order to present a fuzzy relation between a dependent variable and an independent variable. However, in the fuzzy regression analysis the value of the center point of estimated fuzzy output may be either greater than the value of the right endpoint or smaller than the value of the left endpoint. In the case, we cannot predict the fuzzy output properly. This paper presents sufficient conditions to construct the fuzzy regression model using several methods investigated by some authors and then introduces the censored fuzzy regression model using the censored samples to manipulate the problem of crossing of the center and the end points of the estimated fuzzy number. Examples show that the censored fuzzy regression model is an extension of the fuzzy regression model and also it improves the problem of crossing.

Influence diagnostics for skew-t censored linear regression models

  • Marcos S Oliveira;Daniela CR Oliveira;Victor H Lachos
    • Communications for Statistical Applications and Methods
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    • v.30 no.6
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    • pp.605-629
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    • 2023
  • This paper proposes some diagnostics procedures for the skew-t linear regression model with censored response. The skew-t distribution is an attractive family of asymmetrical heavy-tailed densities that includes the normal, skew-normal and student's-t distributions as special cases. Inspired by the power and wide applicability of the EM-type algorithm, local and global influence analysis, based on the conditional expectation of the complete-data log-likelihood function are developed, following Zhu and Lee's approach. For the local influence analysis, four specific perturbation schemes are discussed. Two real data sets, from education and economics, which are right and left censoring, respectively, are analyzed in order to illustrate the usefulness of the proposed methodology.

Regression Quantiles Under Censoring and Truncation

  • Park, Jin-Ho;Kim, Jin-Mi
    • Communications for Statistical Applications and Methods
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    • v.12 no.3
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    • pp.807-818
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    • 2005
  • In this paper we propose an estimation method for regression quantiles with left-truncated and right-censored data. The estimation procedure is based on the weight determined by the Kaplan-Meier estimate of the distribution of the response. We show how the proposed regression quantile estimators perform through analyses of Stanford heart transplant data and AIDS incubation data. We also investigate the effect of censoring on regression quantiles through simulation study.