Applying Multiple Imputation with Geostatistical Models to Account for Item Nonresponse in Environmental Data

被引：7
|
作者：
Munoz, Breda [1 ]
Lesser, Virginia M. [2 ,3 ]
Smith, Ruben A. [4 ]
机构：
[1] RTI Int, RTP, Durham, NC 27709 USA
[2] Oregon State Univ, Survey Res Ctr, Corvallis, OR 97331 USA
[3] Oregon State Univ, Stat, Corvallis, OR 97331 USA
[4] Oregon State Univ, Corvallis, OR 97331 USA
来源：
JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2010年 / 9卷 / 01期
关键词：
Environmental surveys; missing data; nonresponse;
D O I：
10.22237/jmasm/1272687960
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Methods proposed to solve the missing data problem in estimation procedures should consider the type of missing data, the missing data mechanism, the sampling design and the availability of auxiliary variables correlated with the process of interest. This article explores the use of geostatistical models with multiple imputation to deal with missing data in environmental surveys. The method is applied to the analysis of data generated from a probability survey to estimate Coho salmon abundance in streams located in western Oregon watersheds.
引用
收藏
页码：274 / 286
页数：13
相关论文
共 50 条
  • [41] Comparison of the multiple imputation approaches for imputing rainfall data series and their applications to watershed models
    Chen, Lei
    Xu, Jiajia
    Wang, Guobo
    Shen, Zhenyao
    JOURNAL OF HYDROLOGY, 2019, 572 : 449 - 460
  • [42] An efficient monotone data augmentation algorithm for multiple imputation in a class of pattern mixture models
    Tang, Yongqiang
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2017, 27 (04) : 620 - 638
  • [43] Generating multiple imputations for matrix sampling data analyzed with item response models
    Thomas, N
    Gan, N
    JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS, 1997, 22 (04) : 425 - 445
  • [44] Multiple imputation of missing data under missing at random: compatible imputation models are not sufficient to avoid bias if they are mis-specified
    Curnow, Elinor
    Capenter, James R.
    Heron, Jon E.
    Cornish, Rosie P.
    Rach, Stefan
    Didelez, Vanessa
    Langeheine, Malte
    Tilling, Kate
    JOURNAL OF CLINICAL EPIDEMIOLOGY, 2023, 160 : 100 - 109
  • [45] Multiple imputation was an efficient method for harmonizing the Mini-Mental State Examination with missing item-level data
    Burns, Richard A.
    Butterworth, Peter
    Kiely, Kim M.
    Bielak, Allison A. M.
    Luszcz, Mary A.
    Mitchell, Paul
    Christensen, Helen
    Von Sanden, Chwee
    Anstey, Kaarin J.
    JOURNAL OF CLINICAL EPIDEMIOLOGY, 2011, 64 (07) : 787 - 793
  • [46] Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data
    Lim, Aaron J-M
    Cheung, Mike W-L
    BEHAVIOR RESEARCH METHODS, 2022, 54 (03) : 1063 - 1077
  • [47] Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data
    Edwards, Jessie K.
    Cole, Stephen R.
    Troester, Melissa A.
    Richardson, David B.
    AMERICAN JOURNAL OF EPIDEMIOLOGY, 2013, 177 (09) : 904 - 912
  • [48] Evaluating FIML and multiple imputation in joint ordinal-continuous measurements models with missing data
    Aaron J.-M. Lim
    Mike W.-L. Cheung
    Behavior Research Methods, 2022, 54 : 1063 - 1077
  • [49] Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis
    Sulis, Isabella
    Porcu, Mariano
    JOURNAL OF CLASSIFICATION, 2017, 34 (02) : 327 - 359
  • [50] Handling Missing Data in Item Response Theory. Assessing the Accuracy of a Multiple Imputation Procedure Based on Latent Class Analysis
    Isabella Sulis
    Mariano Porcu
    Journal of Classification, 2017, 34 : 327 - 359
12345