• Title/Summary/Keyword: Imputation class

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Weighted Hot-Deck Imputation in Farm and Fishery Household Economy Surveys (농어가경제조사에서 가중핫덱 무응답 대체법의 활용)

  • Kim Kyu-Seong;Lee Kee-Jae;Kim Jin
    • The Korean Journal of Applied Statistics
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    • v.18 no.2
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    • pp.311-328
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    • 2005
  • This paper deals with a treatment of nonresponse in farm and fishery household economy surveys in Korea. Since the samples in two surveys were selected by stratified multi-stage sampling and weighted sample means has been used to estimate the population means, we choose a weighted hot-deck imputation method as an appropriate method for two surveys. We investigate the procedure of the weighted hot-deck as well as an adjusted jackknife method for variance estimation. Through an empirical study we found that the method worked very well in both mean and variance estimation in two surveys. In addition, we presented a procedure of forming imputation class and formed four imputation classes for each survey and then compared them with analysis. As a result, we presented two most efficient imputation classes for two surveys.

Imputation using response probabilities

  • Kim, Jae-Kwang;Park, Hyeon-Ah;Jeon, Jong-Woo
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.10a
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    • pp.207-212
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    • 2003
  • In this paper, we propose a class of imputed estimators using response probability. The proposed estimator can be justified under the response probability model and thus is robust against the failure of the assumed imputation model. We also propose a variance estimator that is justified under the response probability model.

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Association of HLA Genotype and Fulminant Type 1 Diabetes in Koreans

  • Kwak, Soo Heon;Kim, Yoon Ji;Chae, Jeesoo;Lee, Cue Hyunkyu;Han, Buhm;Kim, Jong-Il;Jung, Hye Seung;Cho, Young Min;Park, Kyong Soo
    • Genomics & Informatics
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    • v.13 no.4
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    • pp.126-131
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    • 2015
  • Fulminant type 1 diabetes (T1DM) is a distinct subtype of T1DM that is characterized by rapid onset hyperglycemia, ketoacidosis, absolute insulin deficiency, and near normal levels of glycated hemoglobin at initial presentation. Although it has been reported that class II human leukocyte antigen (HLA) genotype is associated with fulminant T1DM, the genetic predisposition is not fully understood. In this study we investigated the HLA genotype and haplotype in 11 Korean cases of fulminant T1DM using imputation of whole exome sequencing data and compared its frequencies with 413 participants of the Korean Reference Panel. The $HLA-DRB1^*04:05-HLA-DQB1^*04:01$ haplotype was significantly associated with increased risk of fulminant T1DM in Fisher's exact test (odds ratio [OR], 4.11; 95% confidence interval [CI], 1.56 to 10.86; p = 0.009). A histidine residue at $HLA-DR{\beta}1$ position 13 was marginally associated with increased risk of fulminant T1DM (OR, 2.45; 95% CI, 1.01 to 5.94; p = 0.054). Although we had limited statistical power, we provide evidence that HLA haplotype and amino acid change can be a genetic risk factor of fulminant T1DM in Koreans. Further large-scale research is required to confirm these findings.

Development of Truck Axle Load Estimation Model Using Weigh-In-Motion Data (WIM 자료를 활용한 화물차량의 축중량 추정 모형 개발에 관한 연구)

  • Oh, Ju Sam
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.31 no.4D
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    • pp.511-518
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    • 2011
  • Truck weight data are essential for road infrastructure design, maintenance and management. WIM (Weigh-In-Motion) system provides highway planners, researchers and officials with statistical data. Recently high speed WIM data also uses to support a vehicle weight regulation and enforcement activities. This paper aims at developing axle load estimating models with high speed WIM data collected from national highway. We also suggest a method to estimate axle load using simple regression model for WIM system. The model proposed by this paper, resulted in better axle load estimation in all class of vehicle than conventional model. The developed axle load estimating model will used for on-going or re-calibration procedures to ensure an adequate level of WIM system performance. This model can also be used for missing axle load data imputation in the future.