• Journal of the Korean Statistical Society
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• v.21 no.2
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• pp.187-200
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• 1992

Samples are often found to be too heterogeneous to be explained by a one-parameter family of models in the sense that the implicit mean-variance relationship in such a family is violated by the data. This phenomenon is often called over-dispersion. The most frequently used method in dealing with over-dispersion is to mix a one-parameter family creating a two parameter marginal mixture family for the data. In this paper, we investigate performance of estimators such as maximum likelihood estimator, method of moment estimator, and maximum quasi-likelihood estimator in negative binomial and beta-binomial distribution. Simulations are done for various mean parameter and dispersion parameter in both distributions, and we conclude that the moment estimators are very superior in the sense of bias and asymptotic relative efficiency.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.537-548
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• 2011

In this paper following Singh et al. (2008), we propose a modified ratio-product type exponential estimator to estimate the finite population mean $\={Y}$ of the study variable y in presence of non-response in different situations viz. (i) population mean $\={X}$ is known, and (ii) population mean $\={X}$ is unknown. The expressions of biases and mean squared error of the proposed estimators have been obtained under large sample approximation using single as well as double sampling. Some realistic conditions have been obtained under which the proposed estimator is more efficient than usual unbiased estimators, ratio estimators, product estimators and exponential ratio and product estimators reported by Rao (1986) and Singh et al. (2010) are found to be more efficient in many situations.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.159-172
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• 1994

An empirical Bayes estimator of the probability of discovering a new species is proposed when some prior information is available on the number f species. The new estimator is shown via simulations to have only a moderate bias and a smaller RMSE than Good's estimator when the species population follows a truncated geometric distribution.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.975-987
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• 2001

We treat the problem of variance estimation for the estimator of population total, which is derived from the calibration estimation procedure corresponding to the levels of auxiliary information under nonresponse situation. We develop the calibrated variance estimation procedure using the fact that the population total and variance as well as the sample total and variance of the auxiliary variable are known. We show that the proposed variance estimation procedure improves the $Lundst\ddot{o}rm$ and $S\ddot{a}rndal's$ (1999) procedure with respect to the variance and nonresponse bias reduction through the simulation study.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.433-445
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• 2002

Using some available information about the unknown variance $\sigma$$^2$ of a normal distribution with mean $\mu$, a sequential approach is used to estimate $\sigma$$^2$. Two cases have been considered regarding the mean $\mu$ being known or unknown. The mean square error (MSE) of the new estimators are compared to that of the usual estimator of $\sigma$$^2$, namely, the sample variance based on a sample of size equal to the expected sample size. Simulation results indicates that, the new estimator is more efficient than the usual estimator of $\sigma$$^2$whenever the actual value of $\sigma$$^2$ is not too far from the prior information.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.569-575
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• 2018

Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.225-233
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• 2000

In life-testing experiments, in which the longest time an experimental unit is on test is not a failure time, but rather a censored observation. For the situation the Kaplan-Meier estimator is known to be a baised estimator of the survival function. Several modifications of the Kaplan-Meier estimator are examined and compared with bias and mean squared error.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.363-376
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• 1998

The linear smoothing spline estimator is modified to remove boundary bias effects. The resulting estimator can be calculated efficiently using an O(n) algorithm that is developed for the computation of fitted values and associated smoothing parameter selection criteria. The asymptotic properties of the estimator are studied for the case of a uniform design. In this case the mean squared error properties of boundary corrected linear smoothing splines are seen to be asymptotically competitive with those for standard second order kernel smoothers.

• Structural Engineering and Mechanics
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• v.34 no.1
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• pp.1-14
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• 2010

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating between the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.409-420
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• 2020

In business survey, modified cut-off sampling is commonly used to greatly increase the accuracy of the estimation while reducing the number of samples. However, non-response rate of take-all stratum has increased significantly and the sample substitution is not possible because the non-response in the take-all stratum affects the accuracy of the estimation. It is important to adjust the bias appropriately if non-response is affected by the variable of interest. In this study, a bias adjusted estimation is proposed as an appropriate method to deal with a non-response in the take-all stratum. In particular, the estimator proposed by Chung and Shin (2020) was applied to the bias adjustment for the take-all stratum; therefore, we suggest a new method to adjust properly for the take-all stratum. The superiority of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.