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multinomial probit regression  (MathWorks Inc)


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    MathWorks Inc multinomial probit regression
    Multinomial Probit Regression, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/multinomial probit regression/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    multinomial probit regression - by Bioz Stars, 2026-03
    90/100 stars

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    Full and Final Model of Potential Risk Factors Using Independent Multinomial Probit Regression: Lesion Remained Unchanged ( U ) and Deteriorated ( D ) a

    Journal: Journal of Dental Research

    Article Title: Risk Score Algorithm for Treatment of Persistent Apical Periodontitis

    doi: 10.1177/0022034514549559

    Figure Lengend Snippet: Full and Final Model of Potential Risk Factors Using Independent Multinomial Probit Regression: Lesion Remained Unchanged ( U ) and Deteriorated ( D ) a

    Article Snippet: The independent multinomial probit stepwise regression procedure (Stata 12.1, StataCorp LP, USA) was selected since outcome categories were discrete variables with more than 2 outcome categories that did not have a natural ordering, and outcome categories were identified per radiographic evaluation of lesion progression.

    Techniques:

    Full and Final Model of Potential Risk Factors Using Independent Multinomial Probit Regression: Lesion Remained Unchanged ( U ) and Deteriorated ( D ) a

    Journal: Journal of Dental Research

    Article Title: Risk Score Algorithm for Treatment of Persistent Apical Periodontitis

    doi: 10.1177/0022034514549559

    Figure Lengend Snippet: Full and Final Model of Potential Risk Factors Using Independent Multinomial Probit Regression: Lesion Remained Unchanged ( U ) and Deteriorated ( D ) a

    Article Snippet: Model Building and Risk Score Algorithm The independent multinomial probit stepwise regression procedure (Stata 12.1, StataCorp LP, USA) was selected since outcome categories were discrete variables with more than 2 outcome categories that did not have a natural ordering, and outcome categories were identified per radiographic evaluation of lesion progression.

    Techniques: