• Journal of the Korean Mathematical Society
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• v.44 no.3
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• pp.525-539
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• 2007

Consider the regression model $Y_i=g(x_i)+e_i\;for\;i=1,\;2,\;{\ldots},\;n$, where: (1) $x_i$ are fixed design points, (2) $e_i$ are independent random errors with mean zero, (3) g($\cdot$) is unknown regression function defined on [0, 1]. Under $Y_i$ are censored randomly, we discuss the asymptotic normality of the weighted kernel estimators of g when the censored distribution function is known or unknown.

• Journal of Korean Society for Quality Management
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• v.23 no.2
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• pp.91-102
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• 1995

For two independent random variables X and Y, the functional R=Pr[X>Y] is of practical importance in reliability. X can be interpreted as the strength of a component subjected to a stress Y, and R is the component's reliability. In this paper nonparametric approach to estimation of R based on censored observations in the strength variables is analyzed and compared by simulations in the moderate sample sizes.

• Journal of the Korean Mathematical Society
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• v.43 no.4
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• pp.859-869
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• 2006

Let {$X,\;X_n;n\;{\geq}\;1$} be a sequence of ${\imath}.{\imath}.d.$ random variables which belong to the attraction of the normal law, and $X^{(1)}_n,...,X^{(n)}_n$ be an arrangement of $X_1,...,X_n$ in decreasing order of magnitude, i.e., $\|X^{(1)}_n\|{\geq}{\cdots}{\geq}\|X^{(n)}_n\|$. Suppose that {${\gamma}_n$} is a sequence of constants satisfying some mild conditions and d'($t_{nk}$) is an appropriate truncation level, where $n_k=[{\beta}^k]\;and\;{\beta}$ is any constant larger than one. Then we show that the conditionally trimmed sums obeys the self-normalized law of the iterated logarithm (LIL). Moreover, the self-normalized LIL for conditionally censored sums is also discussed.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.655-662
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• 2004

In this paper, we assume that strengths of two components system follow a type II bivariate Pareto model with bivariate type I censored data. And these two components are subjected to a common stress which is independent of the strengths of the components. We obtain estimators for the system reliability based on likelihood function and relative frequency, respectively. Also we construct approximated confidence intervals for the reliability based on maximum likelihood estimator and relative frequency estimator, respectively. Finally we present a numerical study.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.11-21
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• 1997

The mean residual life(MRL) function gives the expected remaining life of a item at age t. In particular F is said to be an increasing intially then decreasing MRL(IDMRL) distribution if there exists a turing point $t^*\ge0$ such that m(s)$\le$ m(t) for 0$$\le s$\le$ t $t^*$, m(s)$\ge$ m(t) for $t^*\le$ s$\le$ t. If the preceding inequality is reversed, F is said to be a decreasing initially then increasing MRL(DIMRL) distribution. Hawkins, et al.(1992) proposed test of H0 : F is exponential versus$H_1$: F is IDMRL, and $H_0$ versus $H_1$' : F is DIMRL when turning point is unknown. Their test is based on a complete random sample $X_1$, …, $X_n$ from F. In this paper, we generalized Hawkins-Kochar-Loader test to random censored data.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.461-469
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• 2003

In this paper, we consider the series and parallel system which include two components. We assume that the lifetimes of two components follow the bivariate Pareto model with random censored data. We obtain the estimators and approximated confidence intervals of the reliabilities for series and parallel systems based on maximum likelihood estimator and the relative frequency, respectively. Also we present a numerical example by giving a data set which is generated by computer.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.37-45
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• 1999

In this paper, we consider two components system having Marshall-Olkin's bivariate exponential model. For the bivariate random censorship, we obtain maximum likelihood estimators of parameters and system reliability. And we propose the methods of homogeniety and independence tests using asymptotic normality. Also we compute the estimators and p-values of the testings through Monte Carlo simulation.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.431-443
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• 2019

Goodness-of-fit techniques are an important topic in statistical analysis. Censored data occur frequently in survival experiments; therefore, many studies are conducted when data are censored. In this paper we mainly consider test statistics based on the empirical distribution function (EDF) to test normal distributions with unknown location and scale parameters when data are randomly censored. The most famous EDF test statistic is the Kolmogorov-Smirnov; in addition, the quadratic statistics such as the $Cram{\acute{e}}r-von$ Mises and the Anderson-Darling statistic are well known. The $Cram{\acute{e}}r-von$ Mises statistic is generalized to randomly censored cases by Koziol and Green (Biometrika, 63, 465-474, 1976). In this paper, we generalize the Anderson-Darling statistic to randomly censored data using the Kaplan-Meier estimator as it was done by Koziol and Green. A simulation study is conducted under a particular censorship model proposed by Koziol and Green. Through a simulation study, the generalized Anderson-Darling statistic shows the best power against almost all alternatives considered among the three EDF statistics we take into account.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.31-38
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• 2003

In this paper, we obtain the estimator of system reliability for the bivariate Pareto model with bivariate type 1 censored data. We obtain the estimators and approximated confidence intervals of the reliability for the parallel system based on likelihood function and the relative frequency, respectively. Also we present a numerical example by giving a data set which is generated by computer.

• Communications for Statistical Applications and Methods
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• v.15 no.2
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• pp.213-228
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• 2008

Using a linear loss function, this paper considers the one-sided testing problem for the exponential distribution via the empirical Bayes(EB) approach. Based on right censored data, we propose an EB test for the exponential parameter and obtain its convergence rate and asymptotic optimality, firstly, under the condition that the censoring distribution is known and secondly, that it is unknown.