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You searched for +publisher:"The Ohio State University" +contributor:("Cudeck, Robert"). Showing records 1 – 5 of 5 total matches.

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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

 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… (more)

Subjects/Keywords: Quantitative Psychology; nonlinear structural equation modeling; SEM

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APA (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

 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 (6th 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 (16th 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 (7th 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

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