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You searched for subject:(Multiple imputation Statistics ). Showing records 1 – 30 of 21202 total matches.

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Oregon State University

1. Amer, Safaa R. Neural network imputation : a new fashion or a good tool.

Degree: PhD, Statistics, 2004, Oregon State University

 Most statistical surveys and data collection studies encounter missing data. A common solution to this problem is to discard observations with missing data while reporting… (more)

Subjects/Keywords: Multiple imputation (Statistics)

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APA (6th Edition):

Amer, S. R. (2004). Neural network imputation : a new fashion or a good tool. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/29926

Chicago Manual of Style (16th Edition):

Amer, Safaa R. “Neural network imputation : a new fashion or a good tool.” 2004. Doctoral Dissertation, Oregon State University. Accessed August 20, 2019. http://hdl.handle.net/1957/29926.

MLA Handbook (7th Edition):

Amer, Safaa R. “Neural network imputation : a new fashion or a good tool.” 2004. Web. 20 Aug 2019.

Vancouver:

Amer SR. Neural network imputation : a new fashion or a good tool. [Internet] [Doctoral dissertation]. Oregon State University; 2004. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1957/29926.

Council of Science Editors:

Amer SR. Neural network imputation : a new fashion or a good tool. [Doctoral Dissertation]. Oregon State University; 2004. Available from: http://hdl.handle.net/1957/29926


Wayne State University

2. Grace, Tammy A. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.

Degree: PhD, Education Evaluation and Research, 2016, Wayne State University

  ABSTRACT THE IMPACT OF MULTIPLE IMPUTATION ON THE TYPE II ERROR RATE OF THE T TEST by TAMMY A. GRACE August 2016 Advisor: Shlomo… (more)

Subjects/Keywords: Multiple Imputation; T Test; Statistics and Probability

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APA (6th Edition):

Grace, T. A. (2016). The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/1536

Chicago Manual of Style (16th Edition):

Grace, Tammy A. “The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.” 2016. Doctoral Dissertation, Wayne State University. Accessed August 20, 2019. https://digitalcommons.wayne.edu/oa_dissertations/1536.

MLA Handbook (7th Edition):

Grace, Tammy A. “The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test.” 2016. Web. 20 Aug 2019.

Vancouver:

Grace TA. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. [Internet] [Doctoral dissertation]. Wayne State University; 2016. [cited 2019 Aug 20]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1536.

Council of Science Editors:

Grace TA. The Impact Of Multiple Imputation On The Type Ii Error Rate Of The T Test. [Doctoral Dissertation]. Wayne State University; 2016. Available from: https://digitalcommons.wayne.edu/oa_dissertations/1536


East Carolina University

3. Siver, Sydney R. Methods for Handling Missing Data for Multiple-Item Questionnaires.

Degree: 2017, East Carolina University

 Missing data is a common problem, especially in the social and behavioral sciences. Modern missing data methods are underutilized in the industrial/organizational psychology and human… (more)

Subjects/Keywords: person mean imputation; Monte Carlo; Missing observations (Statistics); Multiple imputation (Statistics); Questionnaires

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APA (6th Edition):

Siver, S. R. (2017). Methods for Handling Missing Data for Multiple-Item Questionnaires. (Thesis). East Carolina University. Retrieved from http://hdl.handle.net/10342/6517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Siver, Sydney R. “Methods for Handling Missing Data for Multiple-Item Questionnaires.” 2017. Thesis, East Carolina University. Accessed August 20, 2019. http://hdl.handle.net/10342/6517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Siver, Sydney R. “Methods for Handling Missing Data for Multiple-Item Questionnaires.” 2017. Web. 20 Aug 2019.

Vancouver:

Siver SR. Methods for Handling Missing Data for Multiple-Item Questionnaires. [Internet] [Thesis]. East Carolina University; 2017. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10342/6517.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Siver SR. Methods for Handling Missing Data for Multiple-Item Questionnaires. [Thesis]. East Carolina University; 2017. Available from: http://hdl.handle.net/10342/6517

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Stellenbosch University

4. Kotze, Loamie. Markov modelling of disease progression in the presence of missing covariates.

Degree: MCom, Statistics and Actuarial Science, 2019, Stellenbosch University

ENGLISH SUMMARY : Breast cancer is a very prevalent cancer amongst women. The stages of breast cancer are influenced by characteristics such as age, hormone… (more)

Subjects/Keywords: Markov processes; Stochastic processes; Multiple imputation (Statistics); Missing observations (Statistics); Medical statistics  – Statistical methods; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kotze, L. (2019). Markov modelling of disease progression in the presence of missing covariates. (Thesis). Stellenbosch University. Retrieved from http://hdl.handle.net/10019.1/106018

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kotze, Loamie. “Markov modelling of disease progression in the presence of missing covariates.” 2019. Thesis, Stellenbosch University. Accessed August 20, 2019. http://hdl.handle.net/10019.1/106018.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kotze, Loamie. “Markov modelling of disease progression in the presence of missing covariates.” 2019. Web. 20 Aug 2019.

Vancouver:

Kotze L. Markov modelling of disease progression in the presence of missing covariates. [Internet] [Thesis]. Stellenbosch University; 2019. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10019.1/106018.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kotze L. Markov modelling of disease progression in the presence of missing covariates. [Thesis]. Stellenbosch University; 2019. Available from: http://hdl.handle.net/10019.1/106018

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

5. Si, Yajuan. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .

Degree: 2012, Duke University

  The thesis develops nonparametric Bayesian models to handle incomplete categorical variables in data sets with high dimension using the framework of multiple imputation. It… (more)

Subjects/Keywords: Statistics; Categorical; Large scale; Multiple imputation; Nonparametric Bayes; Panel; Refreshment sample

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Si, Y. (2012). Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/5837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Si, Yajuan. “Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .” 2012. Thesis, Duke University. Accessed August 20, 2019. http://hdl.handle.net/10161/5837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Si, Yajuan. “Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies .” 2012. Web. 20 Aug 2019.

Vancouver:

Si Y. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . [Internet] [Thesis]. Duke University; 2012. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10161/5837.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Si Y. Nonparametric Bayesian Methods for Multiple Imputation of Large Scale Incomplete Categorical Data in Panel Studies . [Thesis]. Duke University; 2012. Available from: http://hdl.handle.net/10161/5837

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Washington University in St. Louis

6. Murden, Raphiel. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.

Degree: MA, Mathematics, 2011, Washington University in St. Louis

 Women in the United States are becoming infected with HIV more quickly now than ever before; many of whom are at higher risk because of… (more)

Subjects/Keywords: Statistics; Missing data, Multiple Imputation, HIV, Substance Use Disorder, Women

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Murden, R. (2011). Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. (Thesis). Washington University in St. Louis. Retrieved from https://openscholarship.wustl.edu/etd/496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Murden, Raphiel. “Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.” 2011. Thesis, Washington University in St. Louis. Accessed August 20, 2019. https://openscholarship.wustl.edu/etd/496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Murden, Raphiel. “Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders.” 2011. Web. 20 Aug 2019.

Vancouver:

Murden R. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. [Internet] [Thesis]. Washington University in St. Louis; 2011. [cited 2019 Aug 20]. Available from: https://openscholarship.wustl.edu/etd/496.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Murden R. Examining The Effectiveness Of Multiple Imputation: A Case Study On Hiv Risk Behaviors In Women Receiving Treatment For Substance Use Disorders. [Thesis]. Washington University in St. Louis; 2011. Available from: https://openscholarship.wustl.edu/etd/496

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Arizona State University

7. Mistler, Stephen Andrew. Multilevel multiple imputation: An examination of competing methods.

Degree: Psychology, 2015, Arizona State University

 Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing… (more)

Subjects/Keywords: Statistics; Psychology; Hierarchical; Missing Data; Multilevel Modeling; Multiple Imputation

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APA (6th Edition):

Mistler, S. A. (2015). Multilevel multiple imputation: An examination of competing methods. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/29655

Chicago Manual of Style (16th Edition):

Mistler, Stephen Andrew. “Multilevel multiple imputation: An examination of competing methods.” 2015. Doctoral Dissertation, Arizona State University. Accessed August 20, 2019. http://repository.asu.edu/items/29655.

MLA Handbook (7th Edition):

Mistler, Stephen Andrew. “Multilevel multiple imputation: An examination of competing methods.” 2015. Web. 20 Aug 2019.

Vancouver:

Mistler SA. Multilevel multiple imputation: An examination of competing methods. [Internet] [Doctoral dissertation]. Arizona State University; 2015. [cited 2019 Aug 20]. Available from: http://repository.asu.edu/items/29655.

Council of Science Editors:

Mistler SA. Multilevel multiple imputation: An examination of competing methods. [Doctoral Dissertation]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/29655

8. Gwaze, Arnold Rumosa. A cox proportional hazard model for mid-point imputed interval censored data.

Degree: Faculty of Science and Agriculture, 2011, University of Fort Hare

 There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not… (more)

Subjects/Keywords: Statistics – Econometric models; Survival analysis (Biometry); Mathematical statistics – Data processing; Nonparametric statistics; Sampling (Statistics); Multiple imputation (Statistics)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Gwaze, A. R. (2011). A cox proportional hazard model for mid-point imputed interval censored data. (Thesis). University of Fort Hare. Retrieved from http://hdl.handle.net/10353/385

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Gwaze, Arnold Rumosa. “A cox proportional hazard model for mid-point imputed interval censored data.” 2011. Thesis, University of Fort Hare. Accessed August 20, 2019. http://hdl.handle.net/10353/385.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Gwaze, Arnold Rumosa. “A cox proportional hazard model for mid-point imputed interval censored data.” 2011. Web. 20 Aug 2019.

Vancouver:

Gwaze AR. A cox proportional hazard model for mid-point imputed interval censored data. [Internet] [Thesis]. University of Fort Hare; 2011. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10353/385.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gwaze AR. A cox proportional hazard model for mid-point imputed interval censored data. [Thesis]. University of Fort Hare; 2011. Available from: http://hdl.handle.net/10353/385

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Columbia University

9. Liu, Ying. Statistical Learning Methods for Personalized Medical Decision Making.

Degree: 2016, Columbia University

 The theme of my dissertation is on merging statistical modeling with medical domain knowledge and machine learning algorithms to assist in making personalized medical decisions.… (more)

Subjects/Keywords: Machine learning; Biometry; Therapeutics; Multiple imputation (Statistics); Medical statistics; Machine learning – Statistical methods

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, Y. (2016). Statistical Learning Methods for Personalized Medical Decision Making. (Doctoral Dissertation). Columbia University. Retrieved from https://doi.org/10.7916/D8HH6K22

Chicago Manual of Style (16th Edition):

Liu, Ying. “Statistical Learning Methods for Personalized Medical Decision Making.” 2016. Doctoral Dissertation, Columbia University. Accessed August 20, 2019. https://doi.org/10.7916/D8HH6K22.

MLA Handbook (7th Edition):

Liu, Ying. “Statistical Learning Methods for Personalized Medical Decision Making.” 2016. Web. 20 Aug 2019.

Vancouver:

Liu Y. Statistical Learning Methods for Personalized Medical Decision Making. [Internet] [Doctoral dissertation]. Columbia University; 2016. [cited 2019 Aug 20]. Available from: https://doi.org/10.7916/D8HH6K22.

Council of Science Editors:

Liu Y. Statistical Learning Methods for Personalized Medical Decision Making. [Doctoral Dissertation]. Columbia University; 2016. Available from: https://doi.org/10.7916/D8HH6K22


Uppsala University

10. Bucaro, Orlando Olaya. Predicting risk of cyberbullying victimization using lasso regression.

Degree: Statistics, 2017, Uppsala University

  The increased online presence and use of technology by today’s adolescents has created new places where bullying can occur. The aim of this thesis… (more)

Subjects/Keywords: Multiple imputation; Generalized linear mixed models; Variable selection; Probability Theory and Statistics; Sannolikhetsteori och statistik

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APA (6th Edition):

Bucaro, O. O. (2017). Predicting risk of cyberbullying victimization using lasso regression. (Thesis). Uppsala University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Bucaro, Orlando Olaya. “Predicting risk of cyberbullying victimization using lasso regression.” 2017. Thesis, Uppsala University. Accessed August 20, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bucaro, Orlando Olaya. “Predicting risk of cyberbullying victimization using lasso regression.” 2017. Web. 20 Aug 2019.

Vancouver:

Bucaro OO. Predicting risk of cyberbullying victimization using lasso regression. [Internet] [Thesis]. Uppsala University; 2017. [cited 2019 Aug 20]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bucaro OO. Predicting risk of cyberbullying victimization using lasso regression. [Thesis]. Uppsala University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-338767

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


The Ohio State University

11. Kline, David. Systematically Missing Subject-Level Data in Longitudinal Research Synthesis.

Degree: PhD, Biostatistics, 2015, The Ohio State University

 When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data.… (more)

Subjects/Keywords: Biostatistics; Statistics; multiple imputation; research synthesis; longitudinal data; missing data; individual patient data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kline, D. (2015). Systematically Missing Subject-Level Data in Longitudinal Research Synthesis. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809

Chicago Manual of Style (16th Edition):

Kline, David. “Systematically Missing Subject-Level Data in Longitudinal Research Synthesis.” 2015. Doctoral Dissertation, The Ohio State University. Accessed August 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809.

MLA Handbook (7th Edition):

Kline, David. “Systematically Missing Subject-Level Data in Longitudinal Research Synthesis.” 2015. Web. 20 Aug 2019.

Vancouver:

Kline D. Systematically Missing Subject-Level Data in Longitudinal Research Synthesis. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2019 Aug 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809.

Council of Science Editors:

Kline D. Systematically Missing Subject-Level Data in Longitudinal Research Synthesis. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809


UCLA

12. Yi, Yi. A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.

Degree: Statistics, 2014, UCLA

 Repeated-Measures longitudinal data is common in drug research, where every patient is repeatedly measured across time. Responses could either be continuous variables such as blood… (more)

Subjects/Keywords: Statistics; Pharmaceutical sciences; Biostatistics; Bupropion; Generalized Estimating Equations; Longitudinal Data; Multiple Imputation; Repeated Measures

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APA (6th Edition):

Yi, Y. (2014). A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/94k5p5mx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yi, Yi. “A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.” 2014. Thesis, UCLA. Accessed August 20, 2019. http://www.escholarship.org/uc/item/94k5p5mx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yi, Yi. “A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations.” 2014. Web. 20 Aug 2019.

Vancouver:

Yi Y. A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. [Internet] [Thesis]. UCLA; 2014. [cited 2019 Aug 20]. Available from: http://www.escholarship.org/uc/item/94k5p5mx.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yi Y. A Drug-Dependence Treatment Medication Analysis based on Longitudinal Data with Missing Values using Multiple-Imputation Generalized Estimating Equations. [Thesis]. UCLA; 2014. Available from: http://www.escholarship.org/uc/item/94k5p5mx

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

13. Paiva, Thais Viana. Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies .

Degree: 2014, Duke University

  This thesis presents methods for multiple imputation that can be applied to missing data and data with confidential variables. Imputation is useful for missing… (more)

Subjects/Keywords: Statistics; Confidential data; Missing data; Multiple Imputation; Nonignorable missingness; Spatial model; Synthetic data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Paiva, T. V. (2014). Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/9406

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Paiva, Thais Viana. “Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies .” 2014. Thesis, Duke University. Accessed August 20, 2019. http://hdl.handle.net/10161/9406.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Paiva, Thais Viana. “Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies .” 2014. Web. 20 Aug 2019.

Vancouver:

Paiva TV. Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies . [Internet] [Thesis]. Duke University; 2014. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10161/9406.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Paiva TV. Multiple Imputation Methods for Nonignorable Nonresponse, Adaptive Survey Design, and Dissemination of Synthetic Geographies . [Thesis]. Duke University; 2014. Available from: http://hdl.handle.net/10161/9406

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Iowa State University

14. Fostvedt, Luke Karsten. Mixed effects modeling with missing data using quantile regression and joint modeling.

Degree: 2014, Iowa State University

 This thesis focuses on many different modeling approaches that can be used to evaluate large education data sets. In education research, it is common to… (more)

Subjects/Keywords: Statistics; education data; Mixed effects modeling; multiple imputation; Quantile Regression; Education; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Fostvedt, L. K. (2014). Mixed effects modeling with missing data using quantile regression and joint modeling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/14153

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Fostvedt, Luke Karsten. “Mixed effects modeling with missing data using quantile regression and joint modeling.” 2014. Thesis, Iowa State University. Accessed August 20, 2019. https://lib.dr.iastate.edu/etd/14153.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Fostvedt, Luke Karsten. “Mixed effects modeling with missing data using quantile regression and joint modeling.” 2014. Web. 20 Aug 2019.

Vancouver:

Fostvedt LK. Mixed effects modeling with missing data using quantile regression and joint modeling. [Internet] [Thesis]. Iowa State University; 2014. [cited 2019 Aug 20]. Available from: https://lib.dr.iastate.edu/etd/14153.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Fostvedt LK. Mixed effects modeling with missing data using quantile regression and joint modeling. [Thesis]. Iowa State University; 2014. Available from: https://lib.dr.iastate.edu/etd/14153

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Duke University

15. Akande, Olanrewaju Michael. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys .

Degree: 2019, Duke University

  This thesis develops Bayesian methods for handling unit nonresponse, item nonresponse, and erroneous responses in large scale surveys and censuses containing categorical data. I… (more)

Subjects/Keywords: Statistics; Census; Measurement Error; Missing Data; Multiple Imputation; Survey Nonresponse; Survey Weights

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APA (6th Edition):

Akande, O. M. (2019). Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/18766

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Akande, Olanrewaju Michael. “Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys .” 2019. Thesis, Duke University. Accessed August 20, 2019. http://hdl.handle.net/10161/18766.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Akande, Olanrewaju Michael. “Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys .” 2019. Web. 20 Aug 2019.

Vancouver:

Akande OM. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys . [Internet] [Thesis]. Duke University; 2019. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10161/18766.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Akande OM. Bayesian Models for Imputing Missing Data and Editing Erroneous Responses in Surveys . [Thesis]. Duke University; 2019. Available from: http://hdl.handle.net/10161/18766

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Commonwealth University

16. Zheng, Xiyu. SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.

Degree: MS, Biostatistics, 2016, Virginia Commonwealth University

  Abstract In a two-level hierarchical linear model(HLM2), the outcome as well as covariates may have missing values at any of the levels. One way… (more)

Subjects/Keywords: Missing at Random; Maximum Likelihood; Multiple Imputation; Hierarchical Linear Model; Social Statistics

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APA (6th Edition):

Zheng, X. (2016). SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. (Thesis). Virginia Commonwealth University. Retrieved from https://scholarscompass.vcu.edu/etd/4403

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Thesis, Virginia Commonwealth University. Accessed August 20, 2019. https://scholarscompass.vcu.edu/etd/4403.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zheng, Xiyu. “SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY.” 2016. Web. 20 Aug 2019.

Vancouver:

Zheng X. SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. [Internet] [Thesis]. Virginia Commonwealth University; 2016. [cited 2019 Aug 20]. Available from: https://scholarscompass.vcu.edu/etd/4403.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zheng X. SENSITIVITY ANALYSIS IN HANDLING DISCRETE DATA MISSING AT RANDOM IN HIERARCHICAL LINEAR MODELS VIA MULTIVARIATE NORMALITY. [Thesis]. Virginia Commonwealth University; 2016. Available from: https://scholarscompass.vcu.edu/etd/4403

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Michigan

17. Guo, Ying. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.

Degree: PhD, Biostatistics, 2010, University of Michigan

 In much of applied statistics variables of interest are measured with error. In particular, regression with covariates that are subject to measurement error requires adjustment… (more)

Subjects/Keywords: Missing Data; Measurement Error; Multiple Imputation; Public Health; Statistics and Numeric Data; Health Sciences; Science

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Guo, Y. (2010). Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/77676

Chicago Manual of Style (16th Edition):

Guo, Ying. “Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 20, 2019. http://hdl.handle.net/2027.42/77676.

MLA Handbook (7th Edition):

Guo, Ying. “Multiple Imputation for Measurement Error Correction Based on a Calibration Sample.” 2010. Web. 20 Aug 2019.

Vancouver:

Guo Y. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2027.42/77676.

Council of Science Editors:

Guo Y. Multiple Imputation for Measurement Error Correction Based on a Calibration Sample. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/77676


East Tennessee State University

18. Oketch, Tobias O. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.

Degree: MS, Mathematical Sciences, 2017, East Tennessee State University

  Missing data is one of the challenges we are facing today in modeling valid statistical models. It reduces the representativeness of the data samples.… (more)

Subjects/Keywords: Missing not at random; Missing completely at random; Missing at random; Multiple imputation; Multiple imputation by chained equation; Relative efficiency.; Applied Statistics; Multivariate Analysis; Statistical Models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Oketch, T. O. (2017). Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. (Masters Thesis). East Tennessee State University. Retrieved from https://dc.etsu.edu/etd/3217

Chicago Manual of Style (16th Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Masters Thesis, East Tennessee State University. Accessed August 20, 2019. https://dc.etsu.edu/etd/3217.

MLA Handbook (7th Edition):

Oketch, Tobias O. “Performance of Imputation Algorithms on Artificially Produced Missing at Random Data.” 2017. Web. 20 Aug 2019.

Vancouver:

Oketch TO. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. [Internet] [Masters thesis]. East Tennessee State University; 2017. [cited 2019 Aug 20]. Available from: https://dc.etsu.edu/etd/3217.

Council of Science Editors:

Oketch TO. Performance of Imputation Algorithms on Artificially Produced Missing at Random Data. [Masters Thesis]. East Tennessee State University; 2017. Available from: https://dc.etsu.edu/etd/3217


East Tennessee State University

19. Heidt, Kaitlyn. Comparison of Imputation Methods for Mixed Data Missing at Random.

Degree: MS, Mathematical Sciences, 2019, East Tennessee State University

  A statistician's job is to produce statistical models. When these models are precise and unbiased, we can relate them to new data appropriately. However,… (more)

Subjects/Keywords: Missing data; Multiple imputation methods; Multiple imputation by chained equation; Mixed data; Multivariate Analysis; Physical Sciences and Mathematics; Statistical Methodology; Statistics and Probability

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APA (6th Edition):

Heidt, K. (2019). Comparison of Imputation Methods for Mixed Data Missing at Random. (Masters Thesis). East Tennessee State University. Retrieved from https://dc.etsu.edu/etd/3559

Chicago Manual of Style (16th Edition):

Heidt, Kaitlyn. “Comparison of Imputation Methods for Mixed Data Missing at Random.” 2019. Masters Thesis, East Tennessee State University. Accessed August 20, 2019. https://dc.etsu.edu/etd/3559.

MLA Handbook (7th Edition):

Heidt, Kaitlyn. “Comparison of Imputation Methods for Mixed Data Missing at Random.” 2019. Web. 20 Aug 2019.

Vancouver:

Heidt K. Comparison of Imputation Methods for Mixed Data Missing at Random. [Internet] [Masters thesis]. East Tennessee State University; 2019. [cited 2019 Aug 20]. Available from: https://dc.etsu.edu/etd/3559.

Council of Science Editors:

Heidt K. Comparison of Imputation Methods for Mixed Data Missing at Random. [Masters Thesis]. East Tennessee State University; 2019. Available from: https://dc.etsu.edu/etd/3559


University of Kansas

20. Lang, Kyle Matthew. MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net.

Degree: PhD, Psychology, 2015, University of Kansas

 Correctly specifying the imputation model when conducting multiple imputation remains one of the most significant challenges in missing data analysis. This dissertation introduces a robust… (more)

Subjects/Keywords: Quantitative psychology and psychometrics; Statistics; Bayesian Statistics; Big Data; Missing Data; Multiple Imputation; P >; N; Regularized Regression

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APA (6th Edition):

Lang, K. M. (2015). MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net. (Doctoral Dissertation). University of Kansas. Retrieved from http://hdl.handle.net/1808/19062

Chicago Manual of Style (16th Edition):

Lang, Kyle Matthew. “MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net.” 2015. Doctoral Dissertation, University of Kansas. Accessed August 20, 2019. http://hdl.handle.net/1808/19062.

MLA Handbook (7th Edition):

Lang, Kyle Matthew. “MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net.” 2015. Web. 20 Aug 2019.

Vancouver:

Lang KM. MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net. [Internet] [Doctoral dissertation]. University of Kansas; 2015. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1808/19062.

Council of Science Editors:

Lang KM. MIBEN: Robust Multiple Imputation with the Bayesian Elastic Net. [Doctoral Dissertation]. University of Kansas; 2015. Available from: http://hdl.handle.net/1808/19062


University of Georgia

21. Wang, Qun. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.

Degree: MS, Statistics, 2004, University of Georgia

 The presence of missing or incomplete data is a ubiquitous problem in real world datasets. In the thesis, we apply multiple imputation procedures to analyze… (more)

Subjects/Keywords: multiple imputation

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APA (6th Edition):

Wang, Q. (2004). Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms

Chicago Manual of Style (16th Edition):

Wang, Qun. “Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.” 2004. Masters Thesis, University of Georgia. Accessed August 20, 2019. http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms.

MLA Handbook (7th Edition):

Wang, Qun. “Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables.” 2004. Web. 20 Aug 2019.

Vancouver:

Wang Q. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. [Internet] [Masters thesis]. University of Georgia; 2004. [cited 2019 Aug 20]. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms.

Council of Science Editors:

Wang Q. Investigation of multiple imputation procedures in the presence of missing quantitative and categorical variables. [Masters Thesis]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/wang_qun_200408_ms


Victoria University of Wellington

22. Luo, Maoxin. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.

Degree: 2013, Victoria University of Wellington

 The Food Nutrition Environment Survey (FNES) is a survey of New Zealand early childhood centres and schools and the food and nutritional services that they… (more)

Subjects/Keywords: Multiple imputation; Bayesian; Missing data

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APA (6th Edition):

Luo, M. (2013). Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2796

Chicago Manual of Style (16th Edition):

Luo, Maoxin. “Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.” 2013. Masters Thesis, Victoria University of Wellington. Accessed August 20, 2019. http://hdl.handle.net/10063/2796.

MLA Handbook (7th Edition):

Luo, Maoxin. “Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data.” 2013. Web. 20 Aug 2019.

Vancouver:

Luo M. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. [Internet] [Masters thesis]. Victoria University of Wellington; 2013. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/10063/2796.

Council of Science Editors:

Luo M. Imputation on the Food, Nutrition and Environment Surveys 2007 and 2009 data. [Masters Thesis]. Victoria University of Wellington; 2013. Available from: http://hdl.handle.net/10063/2796


University of Michigan

23. Zhu, Jian. Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm.

Degree: PhD, Biostatistics, 2016, University of Michigan

 The sequential regression multivariate imputation (SRMI, also known as chained equations or fully conditional specifications) is a popular approach for handling missing values in highly… (more)

Subjects/Keywords: Missing Data Multiple Imputation; Sequential Regression Multivariate Imputation; Compatible Conditional Specifications; Block-specific Sequential Regression Multivariate Imputation; Sequential Regression Multivariate Imputation by Quasi-Likelihood Regression Models; Statistics and Numeric Data; Science

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APA (6th Edition):

Zhu, J. (2016). Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/133402

Chicago Manual of Style (16th Edition):

Zhu, Jian. “Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm.” 2016. Doctoral Dissertation, University of Michigan. Accessed August 20, 2019. http://hdl.handle.net/2027.42/133402.

MLA Handbook (7th Edition):

Zhu, Jian. “Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm.” 2016. Web. 20 Aug 2019.

Vancouver:

Zhu J. Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm. [Internet] [Doctoral dissertation]. University of Michigan; 2016. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/2027.42/133402.

Council of Science Editors:

Zhu J. Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm. [Doctoral Dissertation]. University of Michigan; 2016. Available from: http://hdl.handle.net/2027.42/133402


University of Victoria

24. Liu, Yongcai. Survival analysis for breast cancer.

Degree: Dept. of Mathematics and Statistics, 2010, University of Victoria

 This research carries out a survival analysis for patients with breast cancer. The influence of clinical and pathologic features, as well as molecular markers on… (more)

Subjects/Keywords: Survival analysis; Breast cancer; Multiple imputation; Molecular marker; UVic Subject Index::Sciences and Engineering::Mathematics::Mathematical statistics

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APA (6th Edition):

Liu, Y. (2010). Survival analysis for breast cancer. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/3049

Chicago Manual of Style (16th Edition):

Liu, Yongcai. “Survival analysis for breast cancer.” 2010. Masters Thesis, University of Victoria. Accessed August 20, 2019. http://hdl.handle.net/1828/3049.

MLA Handbook (7th Edition):

Liu, Yongcai. “Survival analysis for breast cancer.” 2010. Web. 20 Aug 2019.

Vancouver:

Liu Y. Survival analysis for breast cancer. [Internet] [Masters thesis]. University of Victoria; 2010. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/1828/3049.

Council of Science Editors:

Liu Y. Survival analysis for breast cancer. [Masters Thesis]. University of Victoria; 2010. Available from: http://hdl.handle.net/1828/3049


Harvard University

25. Liublinska, Viktoriia. Sensitivity Analyses in Empirical Studies Plagued with Missing Data.

Degree: PhD, Statistics, 2013, Harvard University

Analyses of data with missing values often require assumptions about missingness mechanisms that cannot be assessed empirically, highlighting the need for sensitivity analyses. However, universal… (more)

Subjects/Keywords: Statistics; clinical trial; graphical sensitivity analysis; missing not at random; multiple imputation; principle stratification; tipping-point analysis

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APA (6th Edition):

Liublinska, V. (2013). Sensitivity Analyses in Empirical Studies Plagued with Missing Data. (Doctoral Dissertation). Harvard University. Retrieved from http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841

Chicago Manual of Style (16th Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Doctoral Dissertation, Harvard University. Accessed August 20, 2019. http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841.

MLA Handbook (7th Edition):

Liublinska, Viktoriia. “Sensitivity Analyses in Empirical Studies Plagued with Missing Data.” 2013. Web. 20 Aug 2019.

Vancouver:

Liublinska V. Sensitivity Analyses in Empirical Studies Plagued with Missing Data. [Internet] [Doctoral dissertation]. Harvard University; 2013. [cited 2019 Aug 20]. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841.

Council of Science Editors:

Liublinska V. Sensitivity Analyses in Empirical Studies Plagued with Missing Data. [Doctoral Dissertation]. Harvard University; 2013. Available from: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11124841


University of Wollongong

26. Lago, Luise Patricia. Imputation of household survey data using mixed models.

Degree: PhD, 2015, University of Wollongong

  Household surveys collect information about a household and data items relating to one or more people within the household. Developing an efficient strategy for… (more)

Subjects/Keywords: Imputation; mixed models; households; statistics

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APA (6th Edition):

Lago, L. P. (2015). Imputation of household survey data using mixed models. (Doctoral Dissertation). University of Wollongong. Retrieved from ; https://ro.uow.edu.au/theses/4369

Chicago Manual of Style (16th Edition):

Lago, Luise Patricia. “Imputation of household survey data using mixed models.” 2015. Doctoral Dissertation, University of Wollongong. Accessed August 20, 2019. ; https://ro.uow.edu.au/theses/4369.

MLA Handbook (7th Edition):

Lago, Luise Patricia. “Imputation of household survey data using mixed models.” 2015. Web. 20 Aug 2019.

Vancouver:

Lago LP. Imputation of household survey data using mixed models. [Internet] [Doctoral dissertation]. University of Wollongong; 2015. [cited 2019 Aug 20]. Available from: ; https://ro.uow.edu.au/theses/4369.

Council of Science Editors:

Lago LP. Imputation of household survey data using mixed models. [Doctoral Dissertation]. University of Wollongong; 2015. Available from: ; https://ro.uow.edu.au/theses/4369


University of the Western Cape

27. Karangwa, Innocent. Imputation techniques for non-ordered categorical missing data .

Degree: 2016, University of the Western Cape

 Missing data are common in survey data sets. Enrolled subjects do not often have data recorded for all variables of interest. The inappropriate handling of… (more)

Subjects/Keywords: Missing data; Multiple imputation; Multiple imputation by chained equations; Multivariate normal imputation

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APA (6th Edition):

Karangwa, I. (2016). Imputation techniques for non-ordered categorical missing data . (Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/5061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Karangwa, Innocent. “Imputation techniques for non-ordered categorical missing data .” 2016. Thesis, University of the Western Cape. Accessed August 20, 2019. http://hdl.handle.net/11394/5061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Karangwa, Innocent. “Imputation techniques for non-ordered categorical missing data .” 2016. Web. 20 Aug 2019.

Vancouver:

Karangwa I. Imputation techniques for non-ordered categorical missing data . [Internet] [Thesis]. University of the Western Cape; 2016. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/11394/5061.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Karangwa I. Imputation techniques for non-ordered categorical missing data . [Thesis]. University of the Western Cape; 2016. Available from: http://hdl.handle.net/11394/5061

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


The Ohio State University

28. Kosler, Joseph Stephen. Multiple comparisons using multiple imputation under a two-way mixed effects interaction model.

Degree: PhD, Statistics, 2006, The Ohio State University

 Missing data is commonplace with both surveys and experiments. For this dissertation, we consider imputation methods founded in Survey Sampling, and assess their performance with… (more)

Subjects/Keywords: Statistics; Multiple Comparisons; Multiple Imputation; Missing Data; Mixed Model; Interaction Effect; RMNI

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APA (6th Edition):

Kosler, J. S. (2006). Multiple comparisons using multiple imputation under a two-way mixed effects interaction model. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1150482904

Chicago Manual of Style (16th Edition):

Kosler, Joseph Stephen. “Multiple comparisons using multiple imputation under a two-way mixed effects interaction model.” 2006. Doctoral Dissertation, The Ohio State University. Accessed August 20, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1150482904.

MLA Handbook (7th Edition):

Kosler, Joseph Stephen. “Multiple comparisons using multiple imputation under a two-way mixed effects interaction model.” 2006. Web. 20 Aug 2019.

Vancouver:

Kosler JS. Multiple comparisons using multiple imputation under a two-way mixed effects interaction model. [Internet] [Doctoral dissertation]. The Ohio State University; 2006. [cited 2019 Aug 20]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1150482904.

Council of Science Editors:

Kosler JS. Multiple comparisons using multiple imputation under a two-way mixed effects interaction model. [Doctoral Dissertation]. The Ohio State University; 2006. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1150482904


University of the Western Cape

29. Brydon, Humphrey Charles. Missing imputation methods explored in big data analytics .

Degree: 2018, University of the Western Cape

 The aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of… (more)

Subjects/Keywords: Missing data; Imputation methods; Multiple imputation; Neural network; Bagging

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APA (6th Edition):

Brydon, H. C. (2018). Missing imputation methods explored in big data analytics . (Thesis). University of the Western Cape. Retrieved from http://hdl.handle.net/11394/6605

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Brydon, Humphrey Charles. “Missing imputation methods explored in big data analytics .” 2018. Thesis, University of the Western Cape. Accessed August 20, 2019. http://hdl.handle.net/11394/6605.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Brydon, Humphrey Charles. “Missing imputation methods explored in big data analytics .” 2018. Web. 20 Aug 2019.

Vancouver:

Brydon HC. Missing imputation methods explored in big data analytics . [Internet] [Thesis]. University of the Western Cape; 2018. [cited 2019 Aug 20]. Available from: http://hdl.handle.net/11394/6605.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Brydon HC. Missing imputation methods explored in big data analytics . [Thesis]. University of the Western Cape; 2018. Available from: http://hdl.handle.net/11394/6605

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Oxford

30. Rombach, Ines. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.

Degree: PhD, 2016, University of Oxford

 Missing data is a potential source of bias in the results of randomised controlled trials (RCTs), which can have a negative impact on guidance derived… (more)

Subjects/Keywords: Multiple imputation; Missing data; Randomised controlled trials

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APA (6th Edition):

Rombach, I. (2016). The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437

Chicago Manual of Style (16th Edition):

Rombach, Ines. “The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.” 2016. Doctoral Dissertation, University of Oxford. Accessed August 20, 2019. http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437.

MLA Handbook (7th Edition):

Rombach, Ines. “The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials.” 2016. Web. 20 Aug 2019.

Vancouver:

Rombach I. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. [Internet] [Doctoral dissertation]. University of Oxford; 2016. [cited 2019 Aug 20]. Available from: http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437.

Council of Science Editors:

Rombach I. The handling, analysis and reporting of missing data in patient reported outcome measures for randomised controlled trials. [Doctoral Dissertation]. University of Oxford; 2016. Available from: http://ora.ox.ac.uk/objects/uuid:1d038192-69ca-4d34-9974-1bc092466dee ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.730437

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