• Title/Summary/Keyword: 이상치 추정

Search Result 585, Processing Time 0.028 seconds

On Rice Estimator in Simple Regression Models with Outliers (이상치가 존재하는 단순회귀모형에서 Rice 추정량에 관해서)

  • Park, Chun Gun
    • The Korean Journal of Applied Statistics
    • /
    • v.26 no.3
    • /
    • pp.511-520
    • /
    • 2013
  • Detection outliers and robust estimators are crucial in regression models with outliers. In such studies the focus is on detecting outliers and estimating the coefficients using leave-one-out. Our study introduces Rice estimator which is an error variance estimator without estimating the coefficients. In particular, we study a comparison of the statistical properties for Rice estimator with and without outliers in simple regression models.

Robust Location Estimation based on TDOA and FDOA using Outlier Detection Algorithm (이상치 검출 알고리즘을 이용한 TDOA와 FDOA 기반 이동 신호원 위치 추정 기법)

  • Yoo, Hogeun;Lee, Jaehoon
    • Journal of Convergence for Information Technology
    • /
    • v.10 no.9
    • /
    • pp.15-21
    • /
    • 2020
  • This paper presents the outlier detection algorithm in the estimation method of a source location and velocity based on two-step weighted least-squares method using time difference of arrival(TDOA) and frequency difference of arrival(FDOA) data. Since the accuracy of the estimated location and velocity of a moving source can be reduced by the outliers of TDOA and FDOA data, it is important to detect and remove the outliers. In this paper, the method to find the minimum inlier data and the method to determine whether TDOA and FDOA data are included in inliers or outliers are presented. The results of numerical simulations show that the accuracy of the estimated location and velocity is improved by removing the outliers of TDOA and FDOA data.

Outlier Reduction using C-SCGP for Target Localization based on RSS/AOA in Wireless Sensor Networks (무선 센서 네트워크에서 C-SCGP를 이용한 RSS/AOA 이상치 제거 기반 표적 위치추정 기법)

  • Kang, SeYoung;Lee, Jaehoon;Song, JongIn;Chung, Wonzoo
    • Journal of Convergence for Information Technology
    • /
    • v.11 no.11
    • /
    • pp.31-37
    • /
    • 2021
  • In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

이상치를 감안한 확률강우분포의 매개변수 추정방법의 적용성 검토

  • Kwon, You Jeong;Seo, Yongwon
    • Proceedings of the Korea Water Resources Association Conference
    • /
    • 2018.05a
    • /
    • pp.319-319
    • /
    • 2018
  • 최근 전 세계적으로 극한수문사상의 증가로 인한 피해의 규모와 빈도가 잦아지고 있다. 기후변화에 관한 정부 간 협의체(IPCC)5차 보고서에 따르면 우리나라는 모든 시나리오 하에서 평균 강수량이 증가하는 지역으로 분류되었다. 특히 강우와 태풍피해가 잦은 7월에서 9월의 강우량이 급격히 증가하는 것으로 나타나며 이는 현재보다 극한수문사상이 더욱 빈번하게 일어날 것이라 예상할 수 있다. 하지만 기존의 매개변수 추정방법은 이상치 산정기준을 넘어서는 극치를 제외하고 확률강우량을 산정하고 있는 실정이다. 따라서 본 연구에서는 이러한 기존의 매개변수 추정방법 보다 극한값에 강건한 MDPDE(minimum density power divergence estimator)를 이용한 매개변수 추정을 사용하여 우리나라 60개 강우관측소의 과거 강우관측자료에 대한 최적조율모수에 대한 빈도별 확률강우량을 추정하여 기존의 방법으로 산정한 확률강우량과 비교하였다. 이상치로 분류할 수 있는 극한수문사상이 발생한 우리나라 31개소에 대하여 MDPDE의 적용성을 검토한 결과 기존의 매개변수 추정방법에 비해 이상치를 포함한 100년 빈도 확률강우량이 약13.3% 감소하는 것으로 나타났다.

  • PDF

The Robust Estimation Method for Analyzing the Financial Time Series Data (재무 시계열 자료 분석을 위한 로버스트 추정방법)

  • Kim, S.
    • The Korean Journal of Applied Statistics
    • /
    • v.21 no.4
    • /
    • pp.561-569
    • /
    • 2008
  • In this paper, we propose the double robust estimators which are the solutions of the double robust estimating equations to analyze and treat the outliers in the stock market data in Korea including the IMF period. The feasibility study shows that the proposed estimators work quitely better than the least squares estimators and the conventional robust estimators.

A Comparative Study of a Robust Estimate Method for Abnormal Traffic Detection (이상 트래픽 탐지를 위한 로버스트 추정 방법 비교 연구)

  • Jung, Jae-Yoon;Kim, Sahm
    • Communications for Statistical Applications and Methods
    • /
    • v.18 no.4
    • /
    • pp.517-525
    • /
    • 2011
  • This paper shows the performance evaluation of a robust estimator based on the GARCH model. We first introduce the method of a robust estimate in the GARCH model and the method of an outlier detection in the GARCH model. The results of the real internet traffic data show the out-performance of the robust estimator over the outlier detection method in the GARCH model. In addition, the method of the robust estimate is less complex than the method of the outlier detection method in the GARCH model.

A Time Series-based Algorithm for Eliminating Outliers of GPS Probe Data (시계열기반의 GPS 프로브 자료의 이상치 제거 알고리즘 개발)

  • Choi, Kee-Choo;Jang, Jeong-A
    • Journal of Korean Society of Transportation
    • /
    • v.22 no.6
    • /
    • pp.67-77
    • /
    • 2004
  • A treatment of outlier has been discussed. Outliers disrupt the reliability of information systems and they should be eliminated prior to the information and/or data fusion. A time series-based elimination algorithm were proposed and prediction interval, as a criterion of acceptable value width, was obtained with the model. Ten actual link values were used and the best model was identified as IMA(1,1). Although the actual verification was difficult in a sense that the matching process between the eliminated data and model data was not readily available, the proposed model can be successfully used in practice with some calibration efforts.

Deep Learning-Based Outlier Detection and Correction for 3D Pose Estimation (3차원 자세 추정을 위한 딥러닝 기반 이상치 검출 및 보정 기법)

  • Ju, Chan-Yang;Park, Ji-Sung;Lee, Dong-Ho
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.11 no.10
    • /
    • pp.419-426
    • /
    • 2022
  • In this paper, we propose a method to improve the accuracy of 3D human pose estimation model in various move motions. Existing human pose estimation models have some problems of jitter, inversion, swap, miss that cause miss coordinates when estimating human poses. These problems cause low accuracy of pose estimation models to detect exact coordinates of human poses. We propose a method that consists of detection and correction methods to handle with these problems. Deep learning-based outlier detection method detects outlier of human pose coordinates in move motion effectively and rule-based correction method corrects the outlier according to a simple rule. We have shown that the proposed method is effective in various motions with the experiments using 2D golf swing motion data and have shown the possibility of expansion from 2D to 3D coordinates.

Outlier detection and treatment in industrial sampling survey (경제조사에서의 이상치 탐지와 처리방법)

  • Joo, Young Sun;Cho, Gyo-Young
    • Journal of the Korean Data and Information Science Society
    • /
    • v.27 no.1
    • /
    • pp.131-142
    • /
    • 2016
  • Outliers in surveys can have a large effect on estimates of totals. This is especially true in business surveys where the populations are drawn are typically skewed. In this paper, we discussed the practical development and implementation of methods to identify and deal with outliers. A detection method is based on quartile method and detected outlier is processed in various ways. The study examines two versions of winsorised estimators with three different cut-off thresholds for each one. For the simulation study, four types of weight transformation function have been considered.

A procedure for simultaneous variable selection, variable transformation and outlier identification in linear regression (선형회귀에서 변수선택, 변수변환과 이상치 탐지의 동시적 수행을 위한 절차)

  • Seo, Han Son;Yoon, Min
    • The Korean Journal of Applied Statistics
    • /
    • v.33 no.1
    • /
    • pp.1-10
    • /
    • 2020
  • We propose a unified approach to variable selection, transformation and outliers in the linear model. The procedure includes a sequential method for outlier detection and a least trimmed squares estimator for variable transformation. It uses all possible subsets regressions for model selection. Some real data analyses and the simulation results are provided to show the efficiency of the methods in the context of the correct variable selection and the fitness of the estimated model.