• Journal of the Korean Data and Information Science Society
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• 제8권2호
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• pp.231-237
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• 1997

The Koziol-Green(KG) model has become an important topic in industrial life testing. In this paper we suggest MLE of the reliability function for the Weibull distribution under the KG model. Futhermore, we compare Kaplan-Meier estimator, Nelson estimator, Cheng & Chang estimator, and Ebrahimi estimator with proposed estimator for the reliability function under the KG model.

• Water Engineering Research
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• 제1권1호
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• pp.49-61
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• 2000

Magnitude-frequency relationships are used in the design of dams, highway bridges, culverts, water supply systems, and flood control structures. In this paper, possible techniques for analyzing flood frequency at a site are presented. A currently used approach to flood frequency analysis is based on the concept of parametric statistical inference. In this analysis, the assumption is make that the distribution function describing flood data in known. However, such an assumption is not always justified. Even though many people have shown that the nonparametric method provides a better fit to the data than the parometric method and gives more reliable flood estimates. the noparpmetric method implies a small probability in extrapolation beyond the highest observed data in the sample. Therefore, a remedy is presented in this paper by introducing an estimator which mixes parametric and nonparametric density estimate.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.103-110
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• 1999

In this paper, we summarize some relative risk estimators under the two sample model with proportional hazard and examine the relative efficiencies of the nonparametric estimators relative to the maximum likelihood estimator of a parametric survival function under random censoring model by comparing their asymptotic variances.

• Journal of the Korean Statistical Society
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• 제22권2호
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• pp.147-157
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• 1993

In the survivla analysis the problem of estimating mean residual life function (MRLF) under random censoring is very important. In this paper we propose and study a nonparametric estimator of MRLF, which is a functional form based on the estimator of the survival function due to Susarla and Van Ryzin (1980). The proposed estimator is shown to be better than some other estimators in terms of mean square errors for the exponential and Weibull cases via Monte Carlo simulation studies.

• Communications for Statistical Applications and Methods
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• 제27권5호
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• pp.511-521
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• 2020

In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.

• Journal of the Korean Statistical Society
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• 제34권1호
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• pp.49-60
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• 2005

In this paper we introduce a nonparametric method for estimating the probability density function of detection distances in line transect sampling. The estimator is obtained using a frequency histogram density estimation method. The asymptotic properties of the proposed estimator are derived and compared with those of the kernel estimator under the assumption that the data collected satisfy the shoulder condition. We found that the asymptotic mean square error (AMSE) of the two estimators have about the same convergence rate. The formula for the optimal histogram bin width is derived which minimizes AMSE. Moreover, the performances of the corresponding k-nearest-neighbor estimators are studied through simulation techniques. In the absence of our knowledge whether the shoulder condition is valid or not a new semi-parametric model is suggested to fit the line transect data. The performances of the proposed two estimators are studied and compared with some existing nonparametric and semiparametric estimators using simulation techniques. The results demonstrate the superiority of the new estimators in most cases considered.

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.939-946
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• 2008

In this paper the support vector method is presented for the probability density function estimation when the sample observations are contaminated with random noise. The performance of the procedure is compared to kernel density estimates by the simulation study.

• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.217-233
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• 1996

This paper gives a rigorous proof of an asymptotic result about bias and variance for a transformation-based nonparametric regression estimator proposed by Park et al (1995).

• Journal of the Korean Data and Information Science Society
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• 제4권
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• pp.97-107
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• 1993

In this paper we propose a nonparametric estimator of the men residual life function (MRLF) on a coherent system under the condition that the component lifetimes are censored by system lifetime. It is shown that the proposed estimator, considered as a function of age t, converges weakly to a Gaussian process on a fixed interval. A consistent estimator of asymptotic variance of the proposed estimator is also given.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.347-353
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• 2004

We propose a simple nonparametric estimator of relative risk in the two sample case of the proportional hazards model for complete data. The asymptotic distribution of this estimator is derived using a functional equation. We obtain the asymptotic normality of the proposed estimator and compare with Begun's estimator by confidence interval through simulations.