• Communications for Statistical Applications and Methods
• /
• v.30 no.6
• /
• pp.605-629
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.12 no.3
• /
• pp.807-818
• /
• 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.

• Communications for Statistical Applications and Methods
• /
• v.1 no.1
• /
• pp.52-56
• /
• 1994

When we have an i.i.d. sample of size n from a continuous distribution, the distribution truncated on the left at the rth order statistic plays an important role in the theoretical analysis of the Type 2 censored data. The charaterization of distributions by the average of the conditional expectation and the average of the conditional information concerning the truncated distribution is studied here.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.4
• /
• pp.755-764
• /
• 2011

In many companies field failure data is used to predict the future number of failures, especially when an unexpected failure mode happens to be a problem. It is because they want to predict the number of spare parts needed and the future quality warranty cost associated with the part based on the predictions of the future number of failures. In this paper field summary data is used to predict the future number of failures based on an appropriate distribution. Other types of data are also investigated to identify the appropriate distribution.