• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.255-264
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• 2015

This paper addressed the problem of estimation of finite population mean in double sampling for stratification. This paper proposed a generalized ratio-cum-product type estimator of population mean. The bias and mean square error of the proposed estimator has been obtained upto the first degree of approximation. A particular member of the proposed generalized estimator was identified and studied from a comparison point of view. It is observed that the identified particular estimator is more efficient than usual unbiased estimator and Ige and Tripathi (1987) estimators. An empirical study was conducted in support of the theoretical findings.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.82-99
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• 1998

In sampling survey the nonresponse reduces the precision of the estimator becuase of the nonresponse bias of the estimator. Deville, et al.(1993) considered the generalized raking procedure with the auxiliary information under five distance measures for reducing the nonresponse bias of the estimator. This paper extends the classical weighting adjustment of Deville, et al.(1993) to the stratified sampling case with three among five measures.

• Journal of the Korean Statistical Society
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• v.12 no.2
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• pp.81-90
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• 1983

The least squares estimator of disturbance variance in a regression model is biased under a serial correlation. Under the assumption of an AR(I), Theil(1971) crudely related the bias with the autocorrelation of the disturbances and the autocorrelation of the explanatory variable for a simple regression. In this paper we derive a relation which relates the bias with the autocorrelation of disturbances and the autocorrelation of explanatory variables for a multiple regression with improved precision.

• International Journal of Reliability and Applications
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• v.12 no.1
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• pp.61-77
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• 2011

One of the most important problems in the estimation of the parameter of the failure model, is the cost of experimental sampling units, which can be reduced by using any prior information available about ${\theta}$, and devising a two-stage pooling shrunken estimation procedure. We have proposed an estimator of the reliability function (R(t)) of the exponential model using two-stage time censored data when a prior value about the unknown parameter (${\theta}$) is available from the past. To compare the performance of the proposed estimator with the classical estimator, computer intensive calculations for bias, mean squared error, relative efficiency, expected sample size and percentage of the overall sample size saved expressions, were done for varying the constants involved in the proposed estimator (${\tilde{R}}$(t)).

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.111-118
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• 2011

This paper suggests a ratio-cum product estimator of a finite population mean using information on the coefficient of variation and the fcoefficient of kurtosis of auxiliary variate in stratified random sampling. Bias and MSE expressions of the suggested estimator are derived up to the first degree of approximation. The suggested estimator has been compared with the combined ratio estimator and several other estimators considered by Kadilar and Cingi (2003). In addition, an empirical study is also provided in support of theoretical findings.

• Journal of the Korean Data and Information Science Society
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• v.5 no.2
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• pp.19-27
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• 1994

It is known that the maximum likelihood method does not provide explicit estimator for the scale parameter of the Weibull distribution based on Type-II censored samples. In this paper we provide an approximate maximum likelihood estimator (AMLE) of the scale parameter of the Weibull distribution with Type-II censoring. We obtain the asymptotic variance and simulate the values of the bias and the variance of this estimator based on 3000 Monte Carlo runs for n = 10(10)30 and r,s = 0(1)4. We also simulate the absolute biases of the MLE and the proposed AMLE for complete samples. It is found that the absolute bias of the AMLE is smaller than the absolute bias of the MLE.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.19-28
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• 2003

Weighting adjustment is a method of improving the efficiency of the estimator by incorporating auxiliary variables at the estimation stage. One commonly used method of weighting adjustment is the poststratification, which is a special case of regression estimation but is relatively feasible in terms of actual implementation. If too many auxiliary variables are used in the poststratification, the bias of the resulting point estimator is no longer negligible and the final weights may have extreme weights. In this study, we propose a method of weight ing adjustment that compromises the efficiency and the bias of the point estimator. A limited simulation study is also presented.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.655-664
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• 2015

A nonresponse adjusted raking ratio estimator that consists of weighting adjustment using estimated response probability and raking procedure is often used to reduce the nonresponse bias and keep the calibration property of the estimator. We investigated asymptotic properties of nonresponse adjusted raking ratio estimator and proposed a variance estimator. A simulation study is used to examine the performance of suggested estimators.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.23-32
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• 2012

This paper deals with the bias and variance of regression coefficient estimators in a finite population. We derive approximate formulas for the bias, variance and mean square error of two estimators when we select a fixed-size inclusion probability proportional to the size sample and then estimate regression coefficients by the ordinary least square estimator as well as the weighted least square estimator based on the selected sample data. Necessary and sufficient conditions for the comparison of the two estimators in terms of variance and mean square error are suggested. In addition, a simple example is introduced to numerically compare the variance and mean square error of the two estimators.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.461-475
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• 1998

In panel survey in which the sample is selected by stratified random sampling, if the sampling units shift from a stratum to others in time, then the movement should be incorporated in the estimation procedures. Dealing with the problem caused by the movement of units across stratum in the updated stratification sample, the bias of the conventional estimator neglecting the movement is investigated, arid the bias-adjusted estimators are proposed. The variance estimator of the suggested estimators is also derived. It is illustrated via a simulation study that the proposed estimators beat the conventional estimator in the sense of bias and mean squared error In particular, when the Neyman allocation is applied in stratified sampling, the proposed estimator is shown much more effective to this end.