• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.283-295
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• 2024

Many studies have been conducted to properly handle nonresponse. Recently, many nonresponse imputation methods have been developed and practically used. Most imputation methods assume MCAR (missing completely at random) or MAR (missing at random). On the contrary, there are relatively few studies on imputation under the assumption of MNAR (missing not at random) or NN (nonignorable nonresponse) that are affected by the study variable. The MNAR causes Bias and reduces the accuracy of imputation whenever response probability is not properly estimated. Lee and Shin (2022) proposed a nonresponse imputation method that can be applied to nonignorable nonresponse assuming homoscedasticity in super-population model. In this paper we propose an generalized version of the imputation method proposed by Lee and Shin (2022) to improve the accuracy of estimation by removing the Bias caused by MNAR under heteroscedasticity. In addition, the superiority of the proposed method is confirmed through simulation studies.

• Journal of Applied Reliability
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• v.14 no.1
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• pp.1-9
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• 2014

One important measure of performance for a repairable system is steady-state availability. In this paper, a method to estimate and establish confidence interval for the steady-state availability under Weibull lifetime and lognormal repair time distributions is proposed. Also, bias and mean squared error of a point estimator for an availability are investigated. In addition, a procedure to derive the sample size and critical value for availability demonstration test is presented and illustrated with a numerical example.

• Communications for Statistical Applications and Methods
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• v.21 no.1
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• pp.1-9
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• 2014

This paper discusses the problem of estimation of finite population mean in double sampling for stratification. In fact, ratio and product type exponential estimators of population mean are proposed in double sampling for stratification. The biases and mean squared errors of proposed estimators are obtained upto the first degree of approximation. The proposed estimators have been compared with usual unbiased estimator, ratio and product estimators in double sampling for stratification. To judge the performance of the proposed estimators an empirical study has been carried out.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.747-754
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• 2003

An enhancement of the probabilistic data association filter is presented for tracking a single maneuvering target in clutter environment. The use of the variable dimensional structure leads the probabilistic data association filter to adjust to real motion of a target. The detection of the maneuver for the model switching is performed by the acceleration estimates taken from a bias estimator of the two stage Kalman filter. The proposed algorithm needs low computational power since it is implemented with a single filtering procedure. A simple Monte Carlo simulation was performed to compare the performance of the proposed algorithm and the IMMPDA filter.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.89-98
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• 1999

In this paper we estimate the reliability of parallel system with two components. We assume that the strengths of these components follow bivariate exponential(BVE) models proposed by Marshall-Olkin(1967) Block-Basu(1974) Freund(1961) and Proschan-Sullo(1974) These two components are subjected to a normally distributed random stress which is independent of the strength of the components. If the strengths ($\textit{X}_1$, $\textit{X}_2$) are subjected to a stress($\textit{Y}$) then the system reliability ($\textit{R}$) is given by $\textit{R}=\textit{P}[\textit{Y} We present some numerical results and compare the bias and the mean square error of the maximum likelihood estimator and proposed estimators for a moderate sized samples when $(\textit{X}_1, \textit{X}_2)$ follow BVE of Marshall-Olkin.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.59-72
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• 1996

The purpose of this study is to make an improvement to the batch-means method, which is a procedure to construct a confidence interval(c.i.) for the steady-state process mean of a stationary simulation output process. In the batch-means method, the data in the output process are grouped into batches. The sequence of means of the data included in individual batches is called a batch-menas process and can be treated as an independently and identically distributed set of variables if each batch includes sufficiently large number of observations. The traditional batch-means method, therefore, uses a batch size as large as possible in order to. destroy the autocovariance remaining in the batch-means process. The c.i. prodedure developed and empirically tested in this study uses a small batch size which can be well fitted by a simple ARMA model, and then utilizes the dependence structure in the fitted model to correct for bias in the variance estimator of the sample mean.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.821-840
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• 2009

This article addresses the problem of estimating a family of general population parameter ${\theta}_{({\alpha},{\beta})}$ using auxiliary information in the presence of measurement errors. The general results are then applied to estimate the coefficient of variation $C_Y$ of the study variable Y using the knowledge of the error variance ${\sigma}^2{_U}$ associated with the study variable Y, Based on large sample approximation, the optimal conditions are obtained and the situations are identified under which the proposed class of estimators would be better than conventional estimator. Application of the main result to bivariate normal population is illustrated.

• Communications for Statistical Applications and Methods
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• v.20 no.4
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• pp.247-257
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• 2013

A variety of practical problems can be addressed in the framework of rotation (successive) sampling. The present work presents a sample rotation pattern where sampling units are drawn on two successive occasions. The problem of estimation of population variance on current (second) occasion in two - occasion successive (rotation) sampling has been considered. A class of estimators has been proposed for population variance that includes many estimators as a particular case. Asymptotic properties of the proposed class of estimators are discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion. Optimum replacement policy is discussed. Results are supported with the empirical means of comparison.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.583-591
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• 2014

In wireless communications, knowledge of the signal-to-noise ratio is required in diverse communication applications. In this paper, we derive the variance of the maximum likelihood estimator in the data-aided and non-data-aided schemes for determining the optimal shrinkage factor. The shrinkage factor is usually the constant that is multiplied by the unbiased estimate and it increases the bias slightly while considerably decreasing the variance so that the overall mean squared error decreases. The closed-form biased estimators for binary-phase-shift-keying and quadrature phase-shift-keying systems are then obtained. Simulation results show that the mean squared error of the proposed method is lower than that of the maximum likelihood method for low and moderate signal-to-noise ratio conditions.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.681-695
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• 2012

In this paper, a family of estimators for the finite population variance investigated by Srivastava and Jhajj (1980) is studied under two different situations of random non-response considered by Tracy and Osahan (1994). Asymptotic expressions for the biases and mean squared errors of members of the proposed family are obtained; in addition, an asymptotic optimum estimator(AOE) is also identified. Estimators suggested by Singh and Joarder (1998) are shown to be members of the proposed family. A correction to the Singh and Joarder (1998) results is also presented.