СИМУЛАЦИИ С ЛИПСВАЩИ СТОЙНОСТИ
Keywords:
СИМУЛАЦИИ, ЛИПСВАЩИ СТОЙНОСТИ
Abstract
В процеса на всяко емпирично изследване изследователският екип се
сблъсква с негативите на появата на липсващи стойности (ЛС). Те са появяват
независимо от това кой и къде провежда изследването. Такива се наблюдават както
при репрезентативни изследвания на НСИ, така и при индивидуалните изследвания
на всеки отделен корпоративен или частен изследовател. Затова и проблемът е във
фокуса на теоретиците и практиците по света през последните 20 години. Той се
оценя като сериозен и предизвиква регулярни изследвания, срещи, дискусии и
публикации.
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Clinical Trails DAta with Attrition and Comparisons to Commonly Used Ad-hoc and Modelbased approaches, Journal of Biopharmaceutical Statistics, 15: 383–402.
9. Di Cecco, D. Filipponi, D. (2012). IMPROVEMENT OF THE TIMELINESS OF THE
ITALIAN BUSINESS REGISTER VIA IMPUTATION OF MISSING DATA, Work Session on
Statistical Data Editing (Oslo, Norway, 24-26 September 2012), CONFERENCE OF
EUROPEAN STATISTICIANS, UNECE
10. del Castillo, P., R. (2012). USE OF MACHINE LEARNING METHODS TO IMPUTE
CATEGORICAL DATA, Work Session on Statistical Data Editing
11. (Oslo, Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN
STATISTICIANS, UNECE
12. Durrant, G. B., (2005). Imputation Methods for Handling Item-Nonresponse in the
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and Southampton Statistical Sciences Research Institute (S3RI), University of
Southampton,.
13. Durrant, G.B., Skinner, C. (2005a): Using Missing data Methods to Correct for
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16. Frick, J. R., Grabka, M. M. (2004), DIW Berlin, Missing Income data in the German
SOEP: Incidence, Imputation and its Impact on the Income distribution.
17. Ghosh-DAstiDAr, B. и Schafer, J. L., (2003) Multiple edit/ Multiple imputation for
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Vol. 98, No. 464, Application and Case Studies
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http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Missing_data/Missing.html
22. Glenn Hui, G., AlDarmaki, H., I. (2012). Editing and Imputation of the 2011 Abu
Dhabi Census. Work Session on Statistical Data Editing (Oslo, Norway, 24-26 September
2012), CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
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91, 3, pp. 559–578
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The Guilford Press.
27. Krajnc, А., Seljak, R. (2012). Editing of Multiple Source Data in the Case of the
Slovenian Agricultural Census 2010, Work Session on Statistical Data Editing (Oslo,
Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN STATISTICIANS,
UNECE
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Jersey: Wiley.
31. MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis, Taylor &
Francis Group, LLC.
32. Nekrašaitė-Liegė, V., Rukšėnaitė, J. (2012). APPLICATION OF THE DEVELOPED
SAS MACRO FOR EDITING AND IMPUTATION AT STATISTICS LITHUANIA, Work
Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
33. Nordbotten, S. (1996) Neural Network Imputation Applied to the Norwegian 1990
Population Census Data. Journal of Official Statistics, Vol. 12, No. 4.
34. Palma, O, Schmitz, C (2012). Editing Census Data: Mexico’s Experience, Work
Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
35. Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation
Modeling (Second Edition). Mahwah, NJ: Lawrence Erlbaum Associates
36. Raghunathan, T.E., Lepkowski, J.M. van Hoewyk M., Solenberger P.W. (2001): A
Multivariate Technique for Multiply Imputing Missing Values using a Sequence of
Regression Models, Survey Methodology, 27, 85-95.
37. Rubin, D.B. (1976). Inference and missing data (with discussion). Biometrika, 63,
581-592.
38. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Survey. New York:
Wiley.
39. Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for
the analysis of missing data. Psychological Methods, 6, 317–329.
40. Scheffer, J. (2002). Dealing with Missing data, Research Letters in the Information
and Mathematical Sciences 3, 153-160
41. Takahashi, М., Ito, Т. (2012). MULTIPLE IMPUTATION OF TURNOVER IN
EDINET DATA: TOWARD THE IMPROVEMENT OF IMPUTATION FOR THE
ECONOMIC CENSUS, Work Session on Statistical Data Editing
42. (Oslo, Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN
STATISTICIANS, UNECE
43. van der Loo, M., de Jonge, E. (2012). Automatic Data Editing with Open Source R,
Work Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
REGISTER-BASED CENSUS . Work Session on Statistical Data Editing (Oslo, Norway,
24-26 September 2012), CONFERENCE OF EUROPEAN
2. Aldrich, S., Wardman, L., Rogers, S. (2012). The Practical Implementation of the
2011 UK Census Imputation Methodology, Work Session on Statistical Data Editing
3. (Oslo, Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN
STATISTICIANS, UNECE
4. Allison, P.D. (2002). Missing Data. Sage University Papers Series on Quantitative
Applications in Social Science, 07-136. Thousand Oaks, CA: Sage.
5. Allison, P. D. (2000). Multiple imputation for missing data: A cautionary tale.
Sociological Methods and Research 28: 301-309.
6. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete
data. In: Advanced structural equation modeling, G. A. Marcoulides and R. E.
Schumacker, eds. Mahwah, New Jersey: Lawrence Erlbaum Associates.
7. Byrne, B. M. (2010) Structural equation modeling with AMOS: basic concepts,
applications, and programming- 2nd ed. Taylor & Francis Group, LLC.
8. Demirtas, H. (2005) Bayesian Analysis oh Hierarchical Pattern-Mixture Models for
Clinical Trails DAta with Attrition and Comparisons to Commonly Used Ad-hoc and Modelbased approaches, Journal of Biopharmaceutical Statistics, 15: 383–402.
9. Di Cecco, D. Filipponi, D. (2012). IMPROVEMENT OF THE TIMELINESS OF THE
ITALIAN BUSINESS REGISTER VIA IMPUTATION OF MISSING DATA, Work Session on
Statistical Data Editing (Oslo, Norway, 24-26 September 2012), CONFERENCE OF
EUROPEAN STATISTICIANS, UNECE
10. del Castillo, P., R. (2012). USE OF MACHINE LEARNING METHODS TO IMPUTE
CATEGORICAL DATA, Work Session on Statistical Data Editing
11. (Oslo, Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN
STATISTICIANS, UNECE
12. Durrant, G. B., (2005). Imputation Methods for Handling Item-Nonresponse in the
Social Sciences: A Methodological Review, ESRC National Centre for Research Methods
and Southampton Statistical Sciences Research Institute (S3RI), University of
Southampton,.
13. Durrant, G.B., Skinner, C. (2005a): Using Missing data Methods to Correct for
Measurement Error in a Distribution Function, Survey Methodology
14. Enders, C. K. (2010) Applied missing data analysis, The Guilford Press
15. Fay, R.E. (1996): Alternative Paradigms for the Analysis of Imputed Survey data,
Journal of the American Statistical Association, 91, 434, 490-498.
16. Frick, J. R., Grabka, M. M. (2004), DIW Berlin, Missing Income data in the German
SOEP: Incidence, Imputation and its Impact on the Income distribution.
17. Ghosh-DAstiDAr, B. и Schafer, J. L., (2003) Multiple edit/ Multiple imputation for
Multivariate Continuous data. Journal of the American Statistical Association, Dec. 2003,
Vol. 98, No. 464, Application and Case Studies
18. Glynn, R. J., Laird, N. M., Rubin, D. B. (1986). Selection modeling versus mixture
modeling with nonignorable nonresponse. In H. Wainer (Ed.), Drawing inferences from
self-selected samples (pp. 115–142). New York: Springer-Verlag.
19. Harrington, D (2009) Confirmatory Factor Analysis, Oxford University Press.
20. Heckman, J. (1976). The common structure of statistical models of truncation,
sample selection and limited dependent variables, and a simple estimator for such models,
Annals of Economic and Social Measurement 5, 475-492.
21. Howell D. C., Statistical Home Page,
http://www.uvm.edu/~dhowell/StatPages/More_Stuff/Missing_data/Missing.html
22. Glenn Hui, G., AlDarmaki, H., I. (2012). Editing and Imputation of the 2011 Abu
Dhabi Census. Work Session on Statistical Data Editing (Oslo, Norway, 24-26 September
2012), CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
23. Kalton, G, Kish, L. (1981) Two efficient random imputation procedures, In Proc.
Survey Res. Meth., p. 146-51. American Statistical Association.
24. Kalton, G. and Kasprzyk, D. (1986) The Treatment of Missing Survey data. Survey
Methodology 12, 1-16.
25. Kim, J. and Fuller, W.A. (2004). Fractional hot deck imputation. Biometrika (2004),
91, 3, pp. 559–578
26. Kline, R. B. (2011) Principles and practice of structural equation modeling. 3rd ed.
The Guilford Press.
27. Krajnc, А., Seljak, R. (2012). Editing of Multiple Source Data in the Case of the
Slovenian Agricultural Census 2010, Work Session on Statistical Data Editing (Oslo,
Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN STATISTICIANS,
UNECE
28. Little, R. J. A. (1988). A test of missing completely at random for multivariate data
with missing values. Journal of the American Statistical Association,83, 1198–1202.
29. Little, R. J. A. (1995). Modeling the drop-out mechanism in repeated-measures
studies. Journal of the Ameri-can Statistical Association,90, 1112–1121
30. Little, R.J.A, Rubin, D.B. (2002). Statistical Analysis with Missing data - 2nd ed., New
Jersey: Wiley.
31. MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis, Taylor &
Francis Group, LLC.
32. Nekrašaitė-Liegė, V., Rukšėnaitė, J. (2012). APPLICATION OF THE DEVELOPED
SAS MACRO FOR EDITING AND IMPUTATION AT STATISTICS LITHUANIA, Work
Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
33. Nordbotten, S. (1996) Neural Network Imputation Applied to the Norwegian 1990
Population Census Data. Journal of Official Statistics, Vol. 12, No. 4.
34. Palma, O, Schmitz, C (2012). Editing Census Data: Mexico’s Experience, Work
Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
35. Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation
Modeling (Second Edition). Mahwah, NJ: Lawrence Erlbaum Associates
36. Raghunathan, T.E., Lepkowski, J.M. van Hoewyk M., Solenberger P.W. (2001): A
Multivariate Technique for Multiply Imputing Missing Values using a Sequence of
Regression Models, Survey Methodology, 27, 85-95.
37. Rubin, D.B. (1976). Inference and missing data (with discussion). Biometrika, 63,
581-592.
38. Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Survey. New York:
Wiley.
39. Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for
the analysis of missing data. Psychological Methods, 6, 317–329.
40. Scheffer, J. (2002). Dealing with Missing data, Research Letters in the Information
and Mathematical Sciences 3, 153-160
41. Takahashi, М., Ito, Т. (2012). MULTIPLE IMPUTATION OF TURNOVER IN
EDINET DATA: TOWARD THE IMPROVEMENT OF IMPUTATION FOR THE
ECONOMIC CENSUS, Work Session on Statistical Data Editing
42. (Oslo, Norway, 24-26 September 2012), CONFERENCE OF EUROPEAN
STATISTICIANS, UNECE
43. van der Loo, M., de Jonge, E. (2012). Automatic Data Editing with Open Source R,
Work Session on Statistical Data Editing (Oslo, Norway, 24-26 September 2012),
CONFERENCE OF EUROPEAN STATISTICIANS, UNECE
Published
2023-02-03
How to Cite
Лазаров, Д. (2023). СИМУЛАЦИИ С ЛИПСВАЩИ СТОЙНОСТИ. Vanguard Scientific Instruments in Management, 7(7). Retrieved from https://vsim-journal.info/index.php?journal=vsim&page=article&op=view&path[]=413
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