• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.507-518
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• 1995

The ordinary least squares based estiamte $S^2$ of the disturbance variance is considered in the linear regression model when the disturbances follow the first-order moving-average process. It is shown that $S^2$ is weakly consistent estimate for the disturbance varaince without any restriction on the regressor matrix X. Also, simple exact bounds on the relative bias of $S^2$ are given in finite sample sizes.

• 대기
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• 제30권4호
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• pp.319-333
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• 2020

Aircraft observations constitute one of the major sources of temperature observations which provide three-dimensional information. But it is well known that the aircraft temperature data have warm bias against sonde observation data, and therefore, the correction of aircraft temperature bias is important to improve the model performance. In this study, the algorithm of the bias correction modified from operational KMA (Korea Meteorological Administration) global model is adopted in the preprocessing of aircraft observations, and the effect of the bias correction of aircraft temperature is investigated by conducting the two experiments. The assimilation with the bias correction showed better consistency in the analysis-forecast cycle in terms of the differences between observations (radiosonde and GPSRO (Global Positioning System Radio Occultation)) and 6h forecast. This resulted in an improved forecasting skill level of the mid-level temperature and geopotential height in terms of the root-mean-square error. It was noted that the benefits of the correction of aircraft temperature bias was the upper-level temperature in the midlatitudes, and this affected various parameters (winds, geopotential height) via the model dynamics.

• International journal of advanced smart convergence
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• 제8권3호
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• pp.46-53
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• 2019

When a partitioned structure is derived from a data set using a clustering algorithm, it is not unusual to have a different set of outcomes when it runs with a different order of data. This problem is known as the order bias problem. Many algorithms in machine learning fields try to achieve optimized result from available training and test data. Optimization is determined by an evaluation function which has also a tendency toward a certain goal. It is inevitable to have a tendency in the evaluation function both for efficiency and for consistency in the result. But its preference for a specific goal in the evaluation function may sometimes lead to unfavorable consequences in the final result of the clustering. To overcome this bias problems, the first clustering process proceeds to construct an initial partition. The initial partition is expected to imply the possible range in the number of final clusters. We apply the data centric sorting to the data objects in the clusters of the partition to rearrange them in a new order. The same clustering procedure is reapplied to the newly arranged data set to build a new partition. We have developed an algorithm that reduces bias effect resulting from how data is fed into the algorithm. Experiment results have been presented to show that the algorithm helps minimize the order bias effects. We have also shown that the current evaluation measure used for the clustering algorithm is biased toward favoring a smaller number of clusters and a larger size of clusters as a result.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제2권1호
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• pp.41-60
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• 1998

This paper derives a consistent and bias corrected extension of Akaike's Information Criterion (AIC), $AIC_{bc}$, based on Kullback-Leibler information. This criterion has terms that penalize the overparametrization more strongly for small and large samples than that of AIC. The overfitting problem of the asymptotically efficient model selection criteria for small and large samples will be overcome. The $AIC_{bc}$ also provides a consistent model order selection. Thus, it is widely applicable to data with small and/or large sample sizes, and to cases where the number of free parameters is a relatively large fraction of the sample size. Relationships with other model selection criteria such as $AIC_c$ of Hurvich, CAICF of Bozdogan and etc. are discussed. Empirical performances of the $AIC_{bc}$ are studied and discussed in better model order choices of a linear regression model using a Monte Carlo experiment.

• 치위생과학회지
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• 제3권2호
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• pp.83-88
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• 2003

치과의료기관의 질관리를 위한 환자만족도 조사의 측정도구를 개발하고 평가하기 위하여 3회의 설문조사를 실시하였다. 51개소의 치과의료기관이 조사에 참아하였으며, 1017명이 응답하였고, 의료기관의 유형과 자료수집 방법에 따라 5개의 표본 집단으로 분류되었다. 표본 집단과 응답자의 특성에 따른 측정값, 신뢰도, 응답편견의 차이와 효과를 비교하기 위하여 정규성검정, t 검정, 분산분석, 카이스케어 검정, Kruscal-Wallis 검정을 실시하였다. 응답 편견과 균형척도에 의한 신뢰도, 타당도, 척도판별력의 저하효과가 추정되었다. 1. 측정 도구 문제를 평가하기 위한 독립 표본의 신뢰도를 비교한 결과 4개의 표본 집단의 내적 일관성의 신뢰도는 비슷한 수준을 보이고 있다. 2. 두 항목의 균형 척도에 대한 긍정 응답 편견률은 20%이며, 교육 수준에 따라 유의한 차이가 나타났다(P<0.05). 3. 환자 관점의 질 평가 척도의 측정값은 독립 표본 사이에 유의한 차이가 나타나지 않았으며, 연령에 따라 20대와 60대 사이에 유의한 차이가 나타나고 있다(P<0.01). 4. Likert 5점 척도로 구성된 총화평정척도의 평균은 4.16으로 좌측으로 편이되고 중앙값에 집중된 높은 첨도의 분포를 보였다. 5. 하위척도 사이의 평균차, 삼사분위값 대 일사분위값의 평균차가 95%의 신뢰구간에 포함되어 측정도구의 판별력은 매우 낮았다.

• 대기
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• 제27권4호
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• pp.467-481
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• 2017

A correction method of reflectivity in partial beam blockage (PBB) area is suggested, which is based on the combination of digital terrain information and self-consistency principle between polarimetric observation. First, the reflectivity was corrected by adding the radar energy loss estimated from beam blockage simulation using digital elevation model (DEM) and beam propagation geometry in standard atmosphere. The additional energy loss by unexpected obstacles and non-standard beam propagation was estimated by using the coefficient between accumulated reflectivity ($Z_H$) and differences of differential phase shift (${\Phi}_{DP}$) along radial direction. The proposed method was applied to operational S-band dual-polarization radar at Jindo and its performance was compared with those of simulation method and self-consistency method for six rainfall cases. When the accumulated reflectivity and increment of ${\Phi}_{DP}$ along radial direction are too small, the self-consistency method has failed to correct the reflectivity while the combined method has corrected the reflectivity bias reasonably. The correction based on beam simulation showed the underestimation. For evaluation of rainfall estimation, the FBs (FRMSEs) of simulation method and self-consistency principle were -0.32 (0.59) and -0.30 (0.57), respectively. The proposed method showed the lowest FB (-0.24) and FRMSE (0.50). The FB and FMSE were improved by about 18% and by 19% in comparison to those before correction (-0.42 and 0.70). We can conclude that the proposed method can improve the accuracy of rainfall estimation in PBB area.

• Journal of Positioning, Navigation, and Timing
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• 제9권3호
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• pp.221-227
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• 2020

In this paper, the Iterated Consider Extended Kalman Filter (ICEKF) is proposed for bearing-only localization of GNSS interference to improve the estimation performance and filter consistency. The ICEKF is an extended version of Consider KF (CKF) for Iterated EKF (IEKF) to consider an effect of bearing measurement bias error to filter covariance. The ICEKF can mitigate the EKF divergence problem which can occur when linearizing the nonlinear bearing measurement by a large initial state error. Also, it can mitigate filter inconsistency problem of EKF and IEKF which can occur when a weakly observable bearing measurement bias error state is not included in filter state vector. The simulation result shows that the localization error of the ICEKF is smaller than the EKF and IEKF, and the Root Mean Square (RMS) estimation error of ICEKF matches the covariance of filter.

• 한국측량학회지
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• 제37권2호
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• pp.55-62
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• 2019

The stereo geometry establishment based on the precise sensor modeling is prerequisite for accurate stereo data processing. Ground control points are generally required for the accurate sensor modeling though it is not possible over the area where the accessibility is limited or reference data is not available. For the areas, the relative orientation should be carried out to improve the geometric consistency between the stereo data though it does not improve the absolute positional accuracy. The relative orientation requires conjugate points that are well distributed over the entire image region. Therefore the automatic conjugate point extraction is required because the manual operation is labor-intensive. In this study, we applied the method consisting of the key point extraction, the search space minimization based on the epipolar line, and the rigorous outlier detection based on the RPCs (Rational Polynomial Coefficients) bias compensation modeling. We tested different parameters of window sizes for Kompsat-2 across track stereo data and analyzed the RPCs precision after the bias compensation for the cases whether the epipolar line information is used or not. The experimental results showed that matching outliers were inevitable for the different matching parameterization but they were successfully detected and removed with the rigorous method for sub-pixel level of stereo RPCs precision.

• 대한원격탐사학회지
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• 제28권1호
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• pp.69-77
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• 2012

표면도달일사량은 전 지구시스템에 대한 기후 연구에 가장 중요한 요소 중 하나다. 기후 연구에서는 동일 지역을 관측하는 두 개 혹은 그 이상의 위성자료로부터 넓은 공간적 범위를 가지는 장기간의 데이터를 사용하는 것이 필요하다. 시간적 연속성을 가지는 서로 다른 위성으로부터 산출 된 표면도달일사량의 연속성과 일관성을 향상시키는 것은 매우 중요하다. 본 연구에서는 물리적 모델을 이용하여 GOES-9과 MTSAT-1R 위성의 중복 관측 기간 동안의 표면도달일사량을 산출하고, 두 위성의 채널자료와 실측치 비교를 통해 위성간의 연속성과 일관성을 향상시키는 방법을 연구하였다. 두 위성의 적외 채널 온도는 매우 잘 일치하는 경향을 보였다 : RMSE=5.595 Kelvin; Bias=2.065 Kelvin. 반면에, 가시채널은 다른 값의 분포를 보였지만 비슷한 경향을 보였다. 그리고 두 위성으로부터 산출 된 표면도달일사량은 실측치와 일치성이 낮았다. 표면도달일사량의 품질 향상을 위해 구름감쇠계수 조정을 통해 표면도달일사량 산출물을 재생산하였다. 그리고 채널 자료의 비교 분석을 통해 GOES-9 위성을 위한 구름감쇠계수를 생산하였다. 그 결과, 구름 효과를 고려한 GOES-9의 표면도달일사량 산출물은 MTSAT-1R과 실측치에 대해 매우 높은 일치성을 보였다 : RMSE=$83.439W\;m^{-2}$; Bias=$27.296W\;m^{-2}$. 구름감쇠계수 조정을 통해 향상 된 정확도는 두 개 이상의 위성으로부터 산출 된 표면도달일사량 산출물의 연속성과 일관성을 향상 시킬 수 있을 것이다.

• 대한통합의학회지
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• 제10권3호
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• pp.37-52
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• 2022

Purpose : The purpose of this meta-analysis was to evaluate the effects of electrical stimulation on patients with non-specific low back pain. Methods : Domestic databases were gathered from studies that conducted clinical trials associated with electrical stimulation and its impact on pain of non-specific low back patients. A total of 681 studies were identified, with 12 studies satisfying the inclusion data. The studies consisted of patient, intervention, comparison, outcome, and study design (PICO-SD). The search outcomes were items associated with low back pain. Cochrane risk of bias 2 (RoB 2) was used to evaluate the quality of 12 randomized controlled trials. Effect sizes (Hedges's g) in this study were computed as the corrected standard mean difference (SMD). A random-effect model was used to analyze the effect size because of the high heterogeneity among the studies. Egger's regression and 'trim-and-fill' tests were carried out to analyze the publication bias. Cumulative meta-analysis and sensitivity analysis were conducted to analyze the effect according to the sample size and the consistency of the effect size. Results : The following factors had a large overall effect size (Hedges's g=1.28, 95 % CI=.20~2.36) involving electrical stimulation on non-specific low back pain. The subgroup analysis all showed a statistical difference in the types of study design, electrical stimulation, and assessment tool. No statistically significant difference was found in the meta-regression analysis. Publican bias was found in the data. Conclusion : The findings in this study indicate that electrical stimulation interventions have a positive effect on patients with non-specific low back pain. However, due to the low quality of studies and publication bias, the results of our study should be interpreted cautiously.