• Communications for Statistical Applications and Methods
• /
• v.21 no.2
• /
• pp.115-124
• /
• 2014

In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum (Y) is randomly left truncated and is sampled if $Y{\geq}T$, where T is the truncation random variable with an unknown distribution. This unknown distribution is estimated with the Lynden-Bell estimator. In particular the normal approximation rate, by choice of the bandwidth, is shown to be close to $n^{-1/6}$ modulo logarithmic term. We have also investigated this normal approximation rate via a simulation study.

• Journal of the Korean Data and Information Science Society
• /
• v.8 no.1
• /
• pp.49-58
• /
• 1997

We consider the quantile function for the bootstrapped product limit estimate under left truncation and right censoring model and show its weak convergence. We also obtain bootstrapped confidence bands for the quantile function.

• Journal of the Korean Data and Information Science Society
• /
• v.6 no.2
• /
• pp.65-76
• /
• 1995

The importance of left truncated and right censoring cases has considered for better information in medical follow-up and engineering life testing studies. We propose some estimation procedure for the mean residual life function with consistency and asymptotic normality on the left truncated and right censoring model. And then, the comparision with Kaplan-Meier estimator ignoring the left truncated effect and the small sample properities are investigated by asymptotic biases and M.S.E.'s thresh Monte Carlo study.

• Journal of the Korean Statistical Society
• /
• v.26 no.3
• /
• pp.333-354
• /
• 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.

• 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.

• 한국데이터정보과학회:학술대회논문집
• /
• 2006.04a
• /
• pp.37-37
• /
• 2006

• Communications for Statistical Applications and Methods
• /
• v.9 no.1
• /
• pp.249-259
• /
• 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.

• Journal of Information Management
• /
• v.23 no.1
• /
• pp.39-62
• /
• 1992

In this paper about 60 auxiliary words and 320 stopwords are selected from analysis of sample data, four types of stop word are classified left, right and - auxiliary word truncation & normal. And a keyword extraction system is suggested which undertakes efficient truncation of auxiliary word from words, conversion of Chinese word to Korean and exclusion of stopword. The selected keyeords in this system show 92.2% of accordance ratio compared with manually selected keywords by expert. And then compound words consist of $4{\sim}6$ character generate twice of additional new words and 58.8% words of those are useful as keyword.

• The Korean Journal of Applied Statistics
• /
• v.19 no.3
• /
• pp.395-406
• /
• 2006

Long-term survival rates are the most commonly used outcome measures for patients with cancer. However, traditional long-term survival statistics, which are derived by cohort analysis or complete analysis, essentially reflect the survival expectations of patients diagnosed many years ago. They are often outdated at the time they become available. In this article, we propose a modified product-limit method to obtain up-to-date estimates of long-term survival rates via a period analysis. The proposed method is illustrated with cancer registry data collected from January 1993 to December 1997.