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You searched for +publisher:"The Ohio State University" +contributor:("Stasny, Elizabeth"). Showing records 1 – 14 of 14 total matches.

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The Ohio State University

1. Wei, Ran. On Estimation Problems in Network Sampling.

Degree: PhD, Statistics, 2016, The Ohio State University

 With the popularity of online social networks such as Facebook, Twitter and LinkedIn, the scale of network data has become enormous. How to take samples… (more)

Subjects/Keywords: Statistics; social networks, network sampling, sampling bias, estimation, machine learning, judgement post-stratification, data mining

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APA (6th Edition):

Wei, R. (2016). On Estimation Problems in Network Sampling. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1471846863

Chicago Manual of Style (16th Edition):

Wei, Ran. “On Estimation Problems in Network Sampling.” 2016. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1471846863.

MLA Handbook (7th Edition):

Wei, Ran. “On Estimation Problems in Network Sampling.” 2016. Web. 19 Sep 2019.

Vancouver:

Wei R. On Estimation Problems in Network Sampling. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1471846863.

Council of Science Editors:

Wei R. On Estimation Problems in Network Sampling. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1471846863


The Ohio State University

2. Sroka, Christopher J. Extending Ranked Set Sampling to Survey Methodology.

Degree: PhD, Statistics, 2008, The Ohio State University

 Ranked set sampling (RSS) is a method of data collection that makes use of the sampler's judgment of relative sizes of potential sample units. In… (more)

Subjects/Keywords: Statistics; Stratified sampling; Ratio estimation; Sample allocation; Simulated annealing

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APA (6th Edition):

Sroka, C. J. (2008). Extending Ranked Set Sampling to Survey Methodology. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1218543909

Chicago Manual of Style (16th Edition):

Sroka, Christopher J. “Extending Ranked Set Sampling to Survey Methodology.” 2008. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218543909.

MLA Handbook (7th Edition):

Sroka, Christopher J. “Extending Ranked Set Sampling to Survey Methodology.” 2008. Web. 19 Sep 2019.

Vancouver:

Sroka CJ. Extending Ranked Set Sampling to Survey Methodology. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1218543909.

Council of Science Editors:

Sroka CJ. Extending Ranked Set Sampling to Survey Methodology. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1218543909


The Ohio State University

3. Kohlschmidt, Jessica Kay. RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS.

Degree: PhD, Statistics, 2009, The Ohio State University

  Ranked set sampling (RSS) is an alternative to simple random sampling that has been shown to outperform simple random sampling (SRS) in many situations.… (more)

Subjects/Keywords: Statistics; Ranked Set Sampling (RSS); Missing Data

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APA (6th Edition):

Kohlschmidt, J. K. (2009). RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1236779896

Chicago Manual of Style (16th Edition):

Kohlschmidt, Jessica Kay. “RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS.” 2009. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236779896.

MLA Handbook (7th Edition):

Kohlschmidt, Jessica Kay. “RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS.” 2009. Web. 19 Sep 2019.

Vancouver:

Kohlschmidt JK. RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS. [Internet] [Doctoral dissertation]. The Ohio State University; 2009. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1236779896.

Council of Science Editors:

Kohlschmidt JK. RANKED SET SAMPLING: A LOOK AT ALLOCATION ISSUES AND MISSING DATA COMPLICATIONS. [Doctoral Dissertation]. The Ohio State University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1236779896


The Ohio State University

4. Modur, Sharada P. Missing Data Methods for Clustered Longitudinal Data.

Degree: PhD, Statistics, 2010, The Ohio State University

  Recently medical and public health research has focused on the development of models for longitudinal studies that aim to identify individuals at risk for… (more)

Subjects/Keywords: Statistics; Longitudinal models; multilevel models; missing data analysis

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APA (6th Edition):

Modur, S. P. (2010). Missing Data Methods for Clustered Longitudinal Data. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785

Chicago Manual of Style (16th Edition):

Modur, Sharada P. “Missing Data Methods for Clustered Longitudinal Data.” 2010. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785.

MLA Handbook (7th Edition):

Modur, Sharada P. “Missing Data Methods for Clustered Longitudinal Data.” 2010. Web. 19 Sep 2019.

Vancouver:

Modur SP. Missing Data Methods for Clustered Longitudinal Data. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785.

Council of Science Editors:

Modur SP. Missing Data Methods for Clustered Longitudinal Data. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785


The Ohio State University

5. Sgambellone, Anthony James. Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples.

Degree: PhD, Statistics, 2013, The Ohio State University

 Judgement post-stratified (JPS) and ranked set sampling (RSS) are known to produce samples that are often more efficient per measurement than simple random sampling due… (more)

Subjects/Keywords: Statistics; RSS; JPS; Order Statistics; Ranking Information; Ranked Set Sample; Judgement Post Stratification

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APA (6th Edition):

Sgambellone, A. J. (2013). Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1386001520

Chicago Manual of Style (16th Edition):

Sgambellone, Anthony James. “Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples.” 2013. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1386001520.

MLA Handbook (7th Edition):

Sgambellone, Anthony James. “Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples.” 2013. Web. 19 Sep 2019.

Vancouver:

Sgambellone AJ. Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples. [Internet] [Doctoral dissertation]. The Ohio State University; 2013. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1386001520.

Council of Science Editors:

Sgambellone AJ. Use of Ranking Information From Unmeasured Units in Ranked Set and Judgement Post Stratified Samples. [Doctoral Dissertation]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1386001520


The Ohio State University

6. Gemayel, Nader M. Bayesian Nonparametric Models for Ranked Set Sampling.

Degree: PhD, Statistics, 2010, The Ohio State University

 Ranked Set Sampling (RSS) is a data collection technique that combines measurement with judgment ranking for statistical inference. After a brief review of the basics… (more)

Subjects/Keywords: Statistics; Ranked Set Sampling; Judgment Ranking; MCMC; Nonparametric Bayes; Judgment Post-stratification; Nonconjugate Models

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APA (6th Edition):

Gemayel, N. M. (2010). Bayesian Nonparametric Models for Ranked Set Sampling. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1271420479

Chicago Manual of Style (16th Edition):

Gemayel, Nader M. “Bayesian Nonparametric Models for Ranked Set Sampling.” 2010. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1271420479.

MLA Handbook (7th Edition):

Gemayel, Nader M. “Bayesian Nonparametric Models for Ranked Set Sampling.” 2010. Web. 19 Sep 2019.

Vancouver:

Gemayel NM. Bayesian Nonparametric Models for Ranked Set Sampling. [Internet] [Doctoral dissertation]. The Ohio State University; 2010. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1271420479.

Council of Science Editors:

Gemayel NM. Bayesian Nonparametric Models for Ranked Set Sampling. [Doctoral Dissertation]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1271420479


The Ohio State University

7. Roberts, Clint Douglas. Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models.

Degree: PhD, Statistics, 2008, The Ohio State University

 The Uniform Crime Reports, collected by the FBI, contain monthly crime counts for each of the seven Index crimes, but for one reason or another,… (more)

Subjects/Keywords: Criminology; Statistics

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APA (6th Edition):

Roberts, C. D. (2008). Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310

Chicago Manual of Style (16th Edition):

Roberts, Clint Douglas. “Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models.” 2008. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310.

MLA Handbook (7th Edition):

Roberts, Clint Douglas. “Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models.” 2008. Web. 19 Sep 2019.

Vancouver:

Roberts CD. Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310.

Council of Science Editors:

Roberts CD. Imputing Missing Values In Time Series Of Count Data Using Hierarchical Models. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1211910310


The Ohio State University

8. Yang, Hui. Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation.

Degree: PhD, Statistics, 2016, The Ohio State University

 In this study, we deal with two problems: rotation group bias and lack of bounding information for producing crime rate estimates using the National Crime… (more)

Subjects/Keywords: Statistics; rotation group bias, zero-inflated count models, multinomial count models, Bayesian models

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APA (6th Edition):

Yang, H. (2016). Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1452167047

Chicago Manual of Style (16th Edition):

Yang, Hui. “Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation.” 2016. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1452167047.

MLA Handbook (7th Edition):

Yang, Hui. “Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation.” 2016. Web. 19 Sep 2019.

Vancouver:

Yang H. Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1452167047.

Council of Science Editors:

Yang H. Adjusting for Bounding and Time-in-Sample Eects in the National Crime Victimization Survey (NCVS) Property Crime Rate Estimation. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1452167047

9. Weston, Daniel Joseph, II. Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique.

Degree: MS, Statistics, 2012, The Ohio State University

 Much health policy research has examined strategies to increase health provider quality and efficiency, and to improve patient outcomes. The American Recovery and Reinvestment Act… (more)

Subjects/Keywords: Statistics; Health Information; Electronic Health Records; Small Area Estimation; Ohio; Regression; EHR; HIT; OSU; Medicaid; GRC; Government Resource Center

…Government Resource Center, The Ohio State University Statistical Consulting Service, and the Ohio… 

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APA (6th Edition):

Weston, Daniel Joseph, I. (2012). Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1325266402

Chicago Manual of Style (16th Edition):

Weston, Daniel Joseph, II. “Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique.” 2012. Masters Thesis, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325266402.

MLA Handbook (7th Edition):

Weston, Daniel Joseph, II. “Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique.” 2012. Web. 19 Sep 2019.

Vancouver:

Weston, Daniel Joseph I. Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique. [Internet] [Masters thesis]. The Ohio State University; 2012. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1325266402.

Council of Science Editors:

Weston, Daniel Joseph I. Improving Estimates for Electronic Health Record Take up in Ohio: A Small Area Estimation Technique. [Masters Thesis]. The Ohio State University; 2012. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1325266402

10. Petraglia, Elizabeth Ellen. Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS).

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

 Crime rates for small geographic areas, or domains, are often of interest in research applications. However, survey data on victimization is often not reliable at… (more)

Subjects/Keywords: Statistics; Criminology; National Crime Victimization Survey; National Incident-Based Reporting System; NCVS; NIBRS; small-area estimation; crime rates; simulation; administrative data; Uniform Crime Reports; UCR

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APA (6th Edition):

Petraglia, E. E. (2015). Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS). (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1439027433

Chicago Manual of Style (16th Edition):

Petraglia, Elizabeth Ellen. “Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS).” 2015. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1439027433.

MLA Handbook (7th Edition):

Petraglia, Elizabeth Ellen. “Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS).” 2015. Web. 19 Sep 2019.

Vancouver:

Petraglia EE. Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS). [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1439027433.

Council of Science Editors:

Petraglia EE. Estimating County-Level Aggravated Assault Rates by Combining Data from the National Crime Victimization Survey (NCVS) and the National Incident-Based Reporting System (NIBRS). [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1439027433

11. Chen, Tian. Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining.

Degree: PhD, Statistics, 2013, The Ohio State University

 Missing data is found in every type of data collection. How to deal with missing data has long been discussed in the survey sampling literature.… (more)

Subjects/Keywords: Statistics; JPS, Machine Learning, Missing Data

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APA (6th Edition):

Chen, T. (2013). Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1374213636

Chicago Manual of Style (16th Edition):

Chen, Tian. “Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining.” 2013. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374213636.

MLA Handbook (7th Edition):

Chen, Tian. “Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining.” 2013. Web. 19 Sep 2019.

Vancouver:

Chen T. Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining. [Internet] [Doctoral dissertation]. The Ohio State University; 2013. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1374213636.

Council of Science Editors:

Chen T. Judgment Post-Stratication with Machine Learning Techniques: Adjusting for Missing Data in Surveys and Data Mining. [Doctoral Dissertation]. The Ohio State University; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1374213636


The Ohio State University

12. Chen, Haiying. Ranked set sampling for binary and ordered categorical variables with applications in health survey data.

Degree: PhD, Biostatistics, 2004, The Ohio State University

 Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling. It involves preliminary ranking of the variable… (more)

Subjects/Keywords: Statistics; RSS; ranking; Neyman; set size; Neyman allocation; SRS

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APA (6th Edition):

Chen, H. (2004). Ranked set sampling for binary and ordered categorical variables with applications in health survey data. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1092770729

Chicago Manual of Style (16th Edition):

Chen, Haiying. “Ranked set sampling for binary and ordered categorical variables with applications in health survey data.” 2004. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1092770729.

MLA Handbook (7th Edition):

Chen, Haiying. “Ranked set sampling for binary and ordered categorical variables with applications in health survey data.” 2004. Web. 19 Sep 2019.

Vancouver:

Chen H. Ranked set sampling for binary and ordered categorical variables with applications in health survey data. [Internet] [Doctoral dissertation]. The Ohio State University; 2004. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1092770729.

Council of Science Editors:

Chen H. Ranked set sampling for binary and ordered categorical variables with applications in health survey data. [Doctoral Dissertation]. The Ohio State University; 2004. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1092770729


The Ohio State University

13. Rumsey, Deborah J. Nonresponse models for social network : stochastic processes.

Degree: PhD, Statistics, 1993, The Ohio State University

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APA (6th Edition):

Rumsey, D. J. (1993). Nonresponse models for social network : stochastic processes. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1261508861

Chicago Manual of Style (16th Edition):

Rumsey, Deborah J. “Nonresponse models for social network : stochastic processes.” 1993. Doctoral Dissertation, The Ohio State University. Accessed September 19, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261508861.

MLA Handbook (7th Edition):

Rumsey, Deborah J. “Nonresponse models for social network : stochastic processes.” 1993. Web. 19 Sep 2019.

Vancouver:

Rumsey DJ. Nonresponse models for social network : stochastic processes. [Internet] [Doctoral dissertation]. The Ohio State University; 1993. [cited 2019 Sep 19]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1261508861.

Council of Science Editors:

Rumsey DJ. Nonresponse models for social network : stochastic processes. [Doctoral Dissertation]. The Ohio State University; 1993. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1261508861


The Ohio State University

14. 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 September 19, 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. 19 Sep 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 Sep 19]. 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

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