• 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.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• 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.

• Communications for Statistical Applications and Methods
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• v.20 no.1
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• pp.63-75
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• 2013

In this paper, we derive maximum likelihood estimators (MLEs) and approximate maximum likelihood estimators (AMLEs) of unknown parameters in a generalized half logistic distribution under Type-II hybrid censoring. We also obtain approximate confidence intervals using asymptotic variance and covariance matrices based on the MLEs and the AMLEs. As an illustration, we examine the validity of the proposed estimation using real data. Finally, we compare the proposed estimators in the sense of the mean squared error (MSE), bias, and length of the approximate confidence interval through a Monte Carlo simulation for various censoring schemes.

• Journal of the Korean Statistical Society
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• v.35 no.3
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• pp.331-341
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• 2006

An approximate distribution of the plug-in estimator of the squared coefficient of variation ($CV^2$) is derived by using Edgeworth expansions under general population models. Also bias of the estimator is investigated for several important distributions. Under the normal distribution, we proposed the new estimator for $CV^2$ based on median of the sampling distribution of plug-in estimator.

• Journal of the Korean Data and Information Science Society
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• v.6 no.1
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• pp.73-83
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• 1995

We construct the approximate bootstrap confidence intervals for reliability (R) when the distributions of strength and stress are both normal. Also we propose percentile, bias correct (BC), bias correct acceleration (BCa), and percentile-t intervals for R. We compare with the accuracy of the proposed bootstrap confidence intervals and classical confidence interval based on asymptotic normal distribution through Monte Carlo simulation. Results indicate that the confidence intervals by bootstrap method work better than classical confidence interval. In particular, confidence intervals by BC and BCa method work well for small sample and/or large value of true reliability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6D
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• pp.1025-1032
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• 2006

Sensor modeling of high-resolution satellites is a prerequisite procedure for mapping and GIS applications. Sensor models, describing the geometric relationship between scene and object, are divided into two main categories, which are rigorous and approximate sensor models. A rigorous model is based on the actual geometry of the image formation process, involving internal and external characteristics of the implemented sensor. However, approximate models require neither a comprehensive understanding of imaging geometry nor the internal and external characteristics of the imaging sensor, which has gathered a great interest within photogrammetric communities. This paper described a comparison between rigorous and various approximate sensor models that have been used to determine three-dimensional positions, and proposed the appropriate sensor model in terms of the satellite imagery usage. Through the case study of using IKONOS satellite scenes, rigorous and approximate sensor models have been compared and evaluated for the positional accuracy in terms of acquirable number of ground controls. Bias compensated RFM(Rational Function Model) turned out to be the best among compared approximate sensor models, both modified parallel projection and parallel-perspective model were able to be modelled with a small number of controls. Also affine transformation, one of the approximate sensor models, can be used to determine the planimetric position of high-resolution satellites and perform image registration between scenes.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.869-878
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• 1998

In this paper, we propose some jackknife estimators for mean in the exponential model with grouped and censored data. Also, we compare the proposed jackknife estimators to other approximate estimators in terms of the mean square error and bias.

• Journal of Sensor Science and Technology
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• v.30 no.4
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• pp.223-228
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• 2021

This work presents a low-power CMOS image sensor (CIS) with a multi-column-parallel (MCP) readout structure while focusing on improving its performance compared to previous works. A delta readout scheme that utilizes the image characteristics is optimized for the MCP readout structure. By simply alternating the MCP readout direction for each row selection, additional memory for the row-to-row delta readout is not required, resulting in a reduced area of occupation compared to the previous work. In addition, the bias current of a pre-amplifier in a successive approximate register (SAR) analog-to-digital converter (ADC) changes according to the operating period to improve the power efficiency. The prototype CIS chip was fabricated using a 0.18-㎛ CMOS process. A 160 × 120 pixel array with 4.4 ㎛ pitch was implemented with a 10-bit SAR ADC. The prototype CIS demonstrated a frame rate of 120 fps with a total power consumption of 1.92 mW.

• KIEE International Transactions on Electrophysics and Applications
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• v.4C no.5
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• pp.220-229
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• 2004

We have examined a technique of one-dimensional (1D) fluid modeling for radio-frequency Ar capacitively coupled plasmas (CCP) between unequal-sized powered and grounded electrodes. In order to simulate a practical CCP reactor configuration with a grounded side wall by the 1D model, it has been assumed that the discharge space has a conic frustum shape; the grounded electrode is larger than the powered one and the discharge space expands with the distance from the powered electrode. In this paper, we focus on how much a 1D model can approximate a 2D model and evaluate their comparisons. The plasma density calculated by the 1D model has been compared with that by a two-dimensional (2D) fluid model, and a qualitative agreement between them has been obtained. In addition, 1D and 2D calculation results for another reactor configuration with equal-sized electrodes have also been presented together for comparison. In the discussion, four CCP models, which are 1D and 2D models with symmetric and asymmetric geometries, are compared with each other and the DC self-bias voltage has been focused on as a characteristic property that reflects the unequal electrode surface areas. Reactor configuration and experimental parameters, which the self-bias depends on, have been investigated to develop the ID modeling for reactor geometry with unequal-sized electrodes.