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The Ohio State University

1. Codd, Casey L. Nonlinear Structural Equation Models: Estimation and Applications.

Degree: MA, Psychology, 2011, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131

► Structural equation modeling is a popular statistical method in the social and behavioral sciences, but it has largely been restricted to modeling linear relationships among…
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Subjects/Keywords: Quantitative Psychology; nonlinear structural equation modeling; SEM

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

APA (6^{th} Edition):

Codd, C. L. (2011). Nonlinear Structural Equation Models: Estimation and Applications. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131

Chicago Manual of Style (16^{th} Edition):

Codd, Casey L. “Nonlinear Structural Equation Models: Estimation and Applications.” 2011. Masters Thesis, The Ohio State University. Accessed February 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131.

MLA Handbook (7^{th} Edition):

Codd, Casey L. “Nonlinear Structural Equation Models: Estimation and Applications.” 2011. Web. 21 Feb 2019.

Vancouver:

Codd CL. Nonlinear Structural Equation Models: Estimation and Applications. [Internet] [Masters thesis]. The Ohio State University; 2011. [cited 2019 Feb 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131.

Council of Science Editors:

Codd CL. Nonlinear Structural Equation Models: Estimation and Applications. [Masters Thesis]. The Ohio State University; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1301409131

2. Bishop, Brenden. Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data.

Degree: MA, Psychology, 2015, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1430998328

► When measuring individuals over time, change is frequently curvilinear and residual errors are typically correlated. Two population-average and two subject-specific models with multiphase mean structures…
(more)

Subjects/Keywords: Psychology; Education; Statistics; Multiphase Models; Piecewise Models; Longitudinal; Population-Average; Subject-Specific; Full Information Maximum Likelihood; Applied Statistics; Bayesian Multilevel Models

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

APA (6^{th} Edition):

Bishop, B. (2015). Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1430998328

Chicago Manual of Style (16^{th} Edition):

Bishop, Brenden. “Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data.” 2015. Masters Thesis, The Ohio State University. Accessed February 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1430998328.

MLA Handbook (7^{th} Edition):

Bishop, Brenden. “Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data.” 2015. Web. 21 Feb 2019.

Vancouver:

Bishop B. Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data. [Internet] [Masters thesis]. The Ohio State University; 2015. [cited 2019 Feb 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1430998328.

Council of Science Editors:

Bishop B. Fitting Statistical Models with Multiphase Mean Structures for Longitudinal Data. [Masters Thesis]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1430998328

3. Bishop, Brenden. Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout.

Degree: PhD, Psychology, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153

► Missing data commonly arise during longitudinal measurements. Dropout is a particulartroublesome type of missingness because inference after the dropout occasionis effectively precluded at the level…
(more)

Subjects/Keywords: Psychology; Pattern-Mixture Model; Longitudinal; Dropout; Missing Data; NMAR; Nonignorable Missingness

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

APA (6^{th} Edition):

Bishop, B. (2017). Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153

Chicago Manual of Style (16^{th} Edition):

Bishop, Brenden. “Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout.” 2017. Doctoral Dissertation, The Ohio State University. Accessed February 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153.

MLA Handbook (7^{th} Edition):

Bishop, Brenden. “Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout.” 2017. Web. 21 Feb 2019.

Vancouver:

Bishop B. Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2019 Feb 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153.

Council of Science Editors:

Bishop B. Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153

4. Farouni, Tarek. An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates.

Degree: PhD, Psychology, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

► The following dissertation consists of two parts. The first part presents an overview of latent variable models from a probabilistic perspective. The main goal of…
(more)

Subjects/Keywords: Statistics; Quantitative Psychology; Bioinformatics; Probabilistic Latent Variable Models; Deep Generative Models; Deep Learning; Chromatin States; Histone Code; Epigenomics

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

APA (6^{th} Edition):

Farouni, T. (2017). An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

Chicago Manual of Style (16^{th} Edition):

Farouni, Tarek. “An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates.” 2017. Doctoral Dissertation, The Ohio State University. Accessed February 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.

MLA Handbook (7^{th} Edition):

Farouni, Tarek. “An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates.” 2017. Web. 21 Feb 2019.

Vancouver:

Farouni T. An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2019 Feb 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539.

Council of Science Editors:

Farouni T. An Overview of Probabilistic Latent Variable Models with anApplication to the Deep Unsupervised Learning of ChromatinStates. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1492189894812539

5. Codd, Casey. A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type.

Degree: PhD, Psychology, 2014, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513

► Models for the joint analysis of multiple outcome variables which are of possibly different scale types are useful because they allow researchers to answer questions…
(more)

Subjects/Keywords: Quantitative Psychology; generalized linear mixed model; longitudinal data analysis; mixed model

…study is managed
by the Center for Human Resource Research at *the* *Ohio* *State* *University*. The…

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

APA (6^{th} Edition):

Codd, C. (2014). A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513

Chicago Manual of Style (16^{th} Edition):

Codd, Casey. “A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type.” 2014. Doctoral Dissertation, The Ohio State University. Accessed February 21, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.

MLA Handbook (7^{th} Edition):

Codd, Casey. “A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type.” 2014. Web. 21 Feb 2019.

Vancouver:

Codd C. A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type. [Internet] [Doctoral dissertation]. The Ohio State University; 2014. [cited 2019 Feb 21]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513.

Council of Science Editors:

Codd C. A Review and Comparison of Models and Estimation Methods for Multivariate Longitudinal Data of Mixed Scale Type. [Doctoral Dissertation]. The Ohio State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1398686513