지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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In statistical theory, an outlier is a value that is numerically distant from overall pattern of adistribution. It may be a meaningful observation, but it comes from an error in survey, data entryand process in most cases. Detection and handling are needed because outliers by errors debasethe statistical quality leading to biased parameter estimation.
Generally, traditional Box-plot or Z-score are very useful for univariate outlier detection and aMedian rule could be applied in the non-Gaussian case. These methods calculate the toleranceinterval that defines the range of acceptable observation values. Outlier detection for periodicsurveys would consider the past view, because it is based on a ratio of value comparing thecurrent time with previous time. If time period, however, is short, a state to get many unchangedvalues can occur. In this case, the ratio is centered at 1, and therefore outlier detection methodreflecting this factor is required.
This paper considers Quartile Method with power transformation and Hidiroglou-Berthelot(1986)method that is efficient in periodic data. The methods were applied to housing sales price. Wesuggest an outlier detection method for real-world data. In addition, we also analyzed data usingTukey Algorithm of United Kingdom's office of National Statistic(ONS).
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참고문헌 (13)
참고문헌 신청
Barnerjee, S. / 2007 / A simple univariate outlier identification procedure designed for large samples / Communication in Statistics-Simulation and Computation 36 : 249 ~ 263
Brys, G. / 2003 / A comparison of some new measures of skewness, In Developments in Roubust Statistics (ICORS 2001) / Springer-Verlag : 98 ~ 113
Carling, K. / 2000 / Resistant outlier rules and the non-Gaussian case / Computational Statistics and Data Analysis 33 : 249 ~ 258
Granquist, L. / 1995 / Improving the traditional editing process, In Business Survey Methods / John Wiley & Sons
Hidiroglou, M. A. / 1986 / Statistical editing and imputation for periodic business surveys / Survey Methodology 12 : 73 ~ 84
Brys, G. / 2003 / A comparison of some new measures of skewness, In Developments in Roubust Statistics (ICORS 2001) / Springer-Verlag : 98 ~ 113
Carling, K. / 2000 / Resistant outlier rules and the non-Gaussian case / Computational Statistics and Data Analysis 33 : 249 ~ 258
Granquist, L. / 1995 / Improving the traditional editing process, In Business Survey Methods / John Wiley & Sons
Hidiroglou, M. A. / 1986 / Statistical editing and imputation for periodic business surveys / Survey Methodology 12 : 73 ~ 84
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