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You searched for id:"oai:digital.library.txstate.edu:10877/8138". One record found.

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Texas State University – San Marcos

1. Zamora, Ryan J. A new 5-step perturbation-based multicollinearity diagnostic package.

Degree: MS, Mathematics, 2019, Texas State University – San Marcos

The ordinary least squares method, for estimating unknown parameters of a multiple linear regression (MLR) model, produces an idealistic solution if the column vectors (regressors) of the design matrix X are linearly independent. However, in a typical MLR setting, true linear independence of the regressors is often an unrealistic situation. Multicollinearity arises as two or more predictor variables departure from linear independence, thus, providing the model with redundant information and causing problems in the MLR parameter estimation and inaccurate statistical inference. The degree of multicollinearity directly reflects the amount of redundancy or interdependence among regressors and the inaccuracy of the MLR inference. Several statistical and analytical detection methods exist and are commonly used to diagnose multicollinearity. These diagnostic methods often produce a measure that reflects the degree of multicollinearity present in the overall model or among the individual regressors. However, these diagnostic methods generally fail to breakdown complex multicollinearity relationships among the regressors. There is also lack of a methodology that combines perturbation analysis with the available diagnostic measures. In addition, several observational strategies are often overlooked and underutilized for diagnosing multicollinearity. Therefore, we develop a new R package, mcperturb, that encompasses several multicollinearity observational strategies and employs a new 5-step perturbation-based method. This package can identify the regressors that may be the main source of the multicollinearity problem. The outputs from the mcperturb package provide a comprehendible opportunity to observe the relatedness between two or more variables on a deeper level than the currently available multicollinearity diagnostic packages. Advisors/Committee Members: Sun, Shuying (advisor), White, Alex (committee member), Zhao, Qiang (committee member).

Subjects/Keywords: Multicollinearity; Perturbation; Diagnostic package; Observational strategies

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

APA (6th Edition):

Zamora, R. J. (2019). A new 5-step perturbation-based multicollinearity diagnostic package. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8138

Chicago Manual of Style (16th Edition):

Zamora, Ryan J. “A new 5-step perturbation-based multicollinearity diagnostic package.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed May 25, 2019. https://digital.library.txstate.edu/handle/10877/8138.

MLA Handbook (7th Edition):

Zamora, Ryan J. “A new 5-step perturbation-based multicollinearity diagnostic package.” 2019. Web. 25 May 2019.

Vancouver:

Zamora RJ. A new 5-step perturbation-based multicollinearity diagnostic package. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2019 May 25]. Available from: https://digital.library.txstate.edu/handle/10877/8138.

Council of Science Editors:

Zamora RJ. A new 5-step perturbation-based multicollinearity diagnostic package. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/8138

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