Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Missing Observations and Data Quality Improvement Read chapter Read first chapter

2013 | OriginalPaper | Chapter

4. Imputation of the Missing Data

Author : Carlos N. Bouza-Herrera

Published in: Handling Missing Data in Ranked Set Sampling

Publisher: Springer Berlin Heidelberg

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We may consider the existence of missing observations as unimportant, considering that the risk of misunderstanding is negligible. The surveyor assumes some model that allows adequately explaining the variable of interest. In such cases, we are able to predict the unknown values and to plug them into some estimator. Generally, the models used for imputing in sampling are not complicated and rely on simple ideas. Imputation in simple random sampling has been developed for decades; the literature is increased yearly. Ranked Set Sampling (RSS) alternatives are presented in this chapter. The efficiency of this approach is supported for the different models. On some occasions the preference of RSS is doubtful and needs numerical comparisons.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter The Non-response Problem: Subsampling Among the Non-respondents
next chapter Some Numerical Studies of the Behavior of RSS
Literature
Al-Nasser, D. A. (2007): L-ranked set sampling: A generalization procedure for robust visual sampling. Communications in Statistics: Simulation and Computation, 6, 33–43.
Al-Omari, A. I., & Jaber, K. (2008). Percentile double ranked set sampling. Journal of Mathematics and Statistics, 4, 60–64.MATH
Al-Omari, A. I., JABER, K., & Al-Omari, A. (2008). Modified ratio-type estimators of the mean using extreme ranked set sampling. Journal of Mathematics and Statistics, 4, 150–155.CrossRefMATH
Al-Omari, A. I., Jemain, A. A., & Ibrahim, K. (2009). New ratio estimators of the mean using simple random sampling and ranked set sampling methods. Revista Investigación Operacional, 30, 97–108.MathSciNetMATH
Bai, Z. D., & Chen, Z. (2003). On the theory of ranked set sampling and its ramifications. Journal of Statistical Planning and Inference, 109, 81–99.MathSciNetCrossRefMATH
Bouza-Herrera C. N. (2012). Estimation of the population mean under l ranked set sampling with missing observations, aproved for publication in. International Journal of Statistics and Probability. doi10.​1155/​2012/​214959
Bouza, C. N., & Al-Omari, A. I. (2011a). Ranked set estimation with imputation of the missing observations: the median estimator. Revista Investigación Operacional, 2, 30–37.
Bouza, C. N., & Al-Omari, A. I. (2011b). Ratio imputation of missing data in ranked set sampling. Submitted to statistics, gsta-2011-0026.r2.
Bouza, C. N., & Al-Omari, A. I. (2013). Imputation methods of missing data for estimating the population mean using simple random sampling with known correlation coefficient. Accepted by Quality and Quantity, 47, 353–365. doi 10.​1007/​s11135-011-9522-1.
Bouza, C. N. (2008a). Estimation of the population mean with missing observations using product type estimators. Revista Investigación Operacional, 29, 207–223.
Chang, H. J., & Huang, K. (2008). Ratio estimation in survey sampling when some observations are missing. International Journal of Information and Management Sciences, 12, 1–9.MathSciNetCrossRef
Chang, H. J., & Huang, K. (2000b). On estimation of ratio of populatoin means in survey sampling when some observations are missing. Journal of Information & Optimization Sciences, 21, 429–436.MathSciNetCrossRefMATH
Chen, Z., Bai, Z., & Sinha, B. K. (2004). Ranked set sampling: theory and applications. Lectures notes in statistics, p. 176. New York: Springer.
Jemain, A. A., & Al-Omari, A. I. (2006). Multistage median ranked set samples for estimating the population mean. Pakistan Journal of Statistics, 22, 195–207.MathSciNetMATH
Kadilar, C., & Cingi, H. (2008). Estimators for the population mean in the case of missing data. Communication in Statistics: Theory and Methods, 37, 2226–2236.MathSciNetCrossRefMATH
Liu, L., TUA, Y., LIB, Y., & ZOU, G. (2006). Imputation for missing data and variance estimation when auxiliary information is incomplete. Model Assisted Statistics and Applications, 1, 83–94.MATH
Mutllak, H. A. (1997). Median ranked set sampling. Journal of Applied Statistical Sciences, 6, 245–255.
Muttlak, H. A. (2003). Investigating the use of quartile ranked set samples for estimating the population mean. Journal of Applied Mathematics and Computation, 146, 437–443.MathSciNetCrossRefMATH
Young-Jae, M. (2005). Monotonicity conditions and inequality imputation for sample and non-response problems. Econometric Reviews, 24, 175–194.MathSciNetCrossRef
Rueda, M., & González, S. (2004). Missing data and auxiliary information in surveys. Computational Statistics, 10, 559–567.
Rueda, M., Martínez, S., Martínez, H., & Arcos, A. (2006). Mean estimation with calibration techniques in presence of missing data. Computational Statistics and Data Analysis, 50, 3263–3277.
Samawi, H. M., & Muttlak, H. A. (1996). Estimation of ratio using rank set sampling. Biometrical Journal, 36, 753–764.MathSciNetCrossRef
Takahasi, K., & Wakimoto, K. (1968). On unbiased estimates of population mean based on the sample stratified by means of ordering. Annals of the Institute of Statistical Mathematics, 20, 1–31.
Toutenburg, H., Srivastava, V. K., & SHALABH, V. (2008). Amputation versus imputation of missing values through ratio method in sample surveys. Statistical Papers, 49, 237–247.MathSciNetCrossRefMATH
Tsukerman, E. V. (2004). Optimal linear estimation of missing observations (in Russian). Studies in information science. Kazan. Lzd. Otechetsvo, 2, 77–96.MathSciNet
Zou, U., Feng, S., & Qin, H. (2002). Sample rotation with missing data. Science in China Series A, 45, 42–63.MathSciNetMATH
Metadata
Title
Imputation of the Missing Data
Author
Carlos N. Bouza-Herrera
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Handling Missing Data in Ranked Set Sampling

Print ISBN: 978-3-642-39898-8

Electronic ISBN: 978-3-642-39899-5

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-39899-5_4

Premium Partner

    Image Credits