• Title/Summary/Keyword: bias estimator

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Jackknife Parametric Estimations in a Truncated Arcsine Distribution

  • Kim, Jung-Dae;Lee, Chang-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.8 no.1
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    • pp.91-97
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    • 1997
  • Maximum likelihood and jackknife estimators of the location and scale parameters and right-tail probability in the truncated arcsine distribution are proposed, and we shall compare the performances of the proposed estimators in terms of bias and mean squared error.

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Utilizing Order Statistics in Density Estimation

  • Kim, W.C.;Park, B.U.
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.227-230
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    • 1995
  • In this paper, we discuss simple ways of implementing non-basic kernel density estimators which typically ceed extra pilot estimation. The methods utilize order statistics at the pilot estimation stages. We focus mainly on bariable lacation and scale kernel density estimator (Jones, Hu and McKay, 1994), but the same idea can be applied to other methods too.

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An Analysis of the Efficiency of Item-based Agricultural Cooperative Using the DEA Model (확률적 DEA모형에 의한 품목농협의 효율성 분석)

  • Lee, Sang-Ho
    • Journal of agriculture & life science
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    • v.45 no.6
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    • pp.279-289
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    • 2011
  • The purpose of this study is to estimate efficiency of item-based agricultural cooperative by using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of item-based agricultural cooperative is 0.80, 0.87, 0.93 respectively. However the bias-corrected estimates are less than those of DEA. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.726 and 0.8747. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.

Estimating survival distributions for two-stage adaptive treatment strategies: A simulation study

  • Vilakati, Sifiso;Cortese, Giuliana;Dlamini, Thembelihle
    • Communications for Statistical Applications and Methods
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    • v.28 no.5
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    • pp.411-424
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    • 2021
  • Inference following two-stage adaptive designs (also known as two-stage randomization designs) with survival endpoints usually focuses on estimating and comparing survival distributions for the different treatment strategies. The aim is to identify the treatment strategy(ies) that leads to better survival of the patients. The objectives of this study were to assess the performance three commonly cited methods for estimating survival distributions in two-stage randomization designs. We review three non-parametric methods for estimating survival distributions in two-stage adaptive designs and compare their performance using simulation studies. The simulation studies show that the method based on the marginal mean model is badly affected by high censoring rates and response rate. The other two methods which are natural extensions of the Nelson-Aalen estimator and the Kaplan-Meier estimator have similar performance. These two methods yield survival estimates which have less bias and more precise than the marginal mean model even in cases of small sample sizes. The weighted versions of the Nelson-Aalen and the Kaplan-Meier estimators are less affected by high censoring rates and low response rates. The bias of the method based on the marginal mean model increases rapidly with increase in censoring rate compared to the other two methods. We apply the three methods to a leukemia clinical trial dataset and also compare the results.

Design of maneuvering target tracking system using neural network as an input estimator (입력 추정기로서의 신경회로망을 이용한 기동 표적 추적 시스템 설계)

  • 김행구;진승희;박진배;주영훈
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.524-527
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    • 1997
  • Conventional target tracking algorithms based on the linear estimation techniques perform quite efficiently when the target motion does not involve maneuvers. Target maneuvers involving short term accelerations, however, cause a bias in the measurement sequence. Accurate compensation for the bias requires processing more samples of which adds to the computational complexity. The primary motivation for employing a neural network for this task comes from the efficiency with which more features can be as inputs for bias compensation. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates and hence can take the burden off the primary Kalman filter which still provides the target position and velocity estimates.

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Comparative Analysis of Predicted Gene Expression among Crenarchaeal Genomes

  • Das, Shibsankar;Chottopadhyay, Brajadulal;Sahoo, Satyabrata
    • Genomics & Informatics
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    • v.15 no.1
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    • pp.38-47
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    • 2017
  • Research into new methods for identifying highly expressed genes in anonymous genome sequences has been going on for more than 15 years. We presented here an alternative approach based on modified score of relative codon usage bias to identify highly expressed genes in crenarchaeal genomes. The proposed algorithm relies exclusively on sequence features for identifying the highly expressed genes. In this study, a comparative analysis of predicted highly expressed genes in five crenarchaeal genomes was performed using the score of Modified Relative Codon Bias Strength (MRCBS) as a numerical estimator of gene expression level. We found a systematic strong correlation between Codon Adaptation Index and MRCBS. Additionally, MRCBS correlated well with other expression measures. Our study indicates that MRCBS can consistently capture the highly expressed genes.

A performance improvement method in the gun fire control system compensating for measurement bias error of the target tracking sensor (표적추적센서의 측정 바이어스 오차 보상에 의한 사격통제장치 성능 향상 기법)

  • Kim, Jae-Hun;Lyou, Joon
    • Journal of the Korea Institute of Military Science and Technology
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    • v.3 no.2
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    • pp.121-130
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    • 2000
  • A practical method is proposed to improve hit probability of the digital gun fire control system, when the measured rate of the tracking sensor becomes biased under some operational situation. For ground moving target it is shown that the well-known Kalman filter which uses position measurement only can be optimally used to eliminate the rate bias error. On the other hand, for 3D moving aircraft we present a new algorithm which incorporate FIR-type filter, which uses position and rate measurement at the same time, and the fixed-lag smoother using position measurement only, and show that it has the optimal performance in terms of both estimation accuracy and response time.

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A study to improve the accuracy of the naive propensity score adjusted estimator using double post-stratification method (나이브 성향점수보정 추정량의 정확성 향상을 위한 이중 사후층화 방법 연구)

  • Leesu Yeo;Key-Il Shin
    • The Korean Journal of Applied Statistics
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    • v.36 no.6
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    • pp.547-559
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    • 2023
  • Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

Generalized Composite Estimators and Mean Squared Errors for l/G Rotation Design (l/G 교체표본디자인에서의 일반화복합추정량과 평균제곱오차에 관한 연구)

  • 김기환;박유성;남궁재은
    • The Korean Journal of Applied Statistics
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    • v.17 no.1
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    • pp.61-73
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    • 2004
  • Rotation sampling designs may be classified into two categories. The first type uses the same sample unit for the entire life of the survey. The second type uses the sample unit only for a fixed number of times. In both type of designs, the entire sample is partitioned into a finite number(=G) of rotation groups. This paper is generalization of the first type designs. Since the generalized design can be identified by only G rotation groups and recall level 1, we denote this rotation system as l/G rotation design. Under l/G rotation design, variance and mean squared error (MSE) of generalized composite estimator are derived, incorporating two type of biases and exponentially decaying correlation pattern. Compromising MSE's of some selected l/G designs, we investigate design efficiency, design gap effect, ans the effects of correlation and bias.

A study on non-response bias adjusted estimation in business survey (사업체조사에서의 무응답 편향보정 추정에 관한 연구)

  • Chung, Hee Young;Shin, Key-Il
    • The Korean Journal of Applied Statistics
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    • v.33 no.1
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    • pp.11-23
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    • 2020
  • Sampling design should provide statistics to meet a given accuracy while saving cost and time. However, a large number of non-responses are occurring due to the deterioration of survey circumstances, which significantly reduces the accuracy of the survey results. Non-responses occur for a variety of reasons. Chung and Shin (2017, 2019) and Min and Shin (2018) found that the accuracy of estimation is improved by removing the bias caused by non-response when the response rate is an exponential or linear function of variable of interests. For that case they assumed that the error of the super population model follows normal distribution. In this study, we proposed a non-response bias adjusted estimator in the case where the error of a super population model follows the gamma distribution or the log-normal distribution in a business survey. We confirmed the superiority of the proposed estimator through simulation studies.