Jacknife and Regression Approaches to Missing Data Imputation.

Authors

  • Jumlong Vongprasert

Keywords:

Missing Data,, Imputation, Jacknife.

Abstract

Missing data imputation is an important task in cases where it is crucial to use all available data
and not discard records with missing values. The purpose of this work were first to develop the
Average of Jacknife Mean and Regression (AJRI) for missing data estimation and secondly to
compare its efficiency of estimation with another methods, namely; Mean Imputation ( MI)
Regression Imputation ( RI) Regime Switching Regression Imputation (RSRI) EM Algorithm
(EM) and Multiple Imputation (MUL) . By using simulation data, the comparisons were made
with the following conditions: ( i) Four sample size (100, 200 500 and 1,000) ( ii) three type of
missing data MCAR MAR and NMAR. The best imputation under MSE. The best imputation
under MSE of mean variance and correlation classified by sample sizes and by percentage of
missing data were obtained using RSRI EM and RI respectively, classified by missing data type
were obtained using RSRI for mean and EM for variance and correlation. The best imputation
under MSE of regression coefficient were obtained using EM and RSRI for model 1 and model
2 respectively. The best imputation under MSE of R2 were obtained using EM.

Author Biography

Jumlong Vongprasert

Applied Statistics Department, Ubon Ratchathani Rajabhat University

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Published

2019-01-17

How to Cite

Vongprasert, J. (2019). Jacknife and Regression Approaches to Missing Data Imputation. Journal of Applied Statistics and Information Technology, 3(1), 52–61. Retrieved from https://ph02.tci-thaijo.org/index.php/asit-journal/article/view/166906

Issue

Vol. 3 No. 1 (2018): Journal of Applied Statistics and Information Technology Vol 3 No 1 (January - June 2018)

Section

Research Articles

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เนื้อหาและข้อมูลที่ปรากฏในบทความที่ตีพิมพ์ในวารสารสถิติประยุกต์และเทคโนโลยีสารสนเทศถือเป็นความคิดเห็นส่วนบุคคลของผู้เขียนแต่ละท่าน ความผิดพลาดของข้อความและผลที่อาจเกิดจากนำข้อความเหล่านั้นไปใช้ผู้เขียนบทความจะเป็นผู้รับผิดชอบแต่เพียงผู้เดียว บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารถือเป็นลิขสิทธิ์ของวารสาร หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะต้องได้รับอนุญาตเป็นลายลักอักษรณ์จากวารสาร ก่อนเท่านั้น