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Biostatistics 305. Multinomial logistic regression.
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Multinomial and ordinal logistic regression
PM&R, 2021Standard logistic regression handles binary outcomes such as disease/no disease. Researchers sometimes collapse outcomes with more than two groups into binary variables to enable use of this familiar model. However, this practice risks losing information
Kristin L. Sainani
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Multinomial Regression Versus Quantile
Quantile Regression in Clinical Research, 2021Ton J. Cleophas, Aeilko H. Zwinderman
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Aslib Journal of Information Management, 2022
PurposeThe purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline.
Sukjin You, Soohyung Joo, Marie Katsurai
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PurposeThe purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline.
Sukjin You, Soohyung Joo, Marie Katsurai
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International Journal of Crashworthiness, 2022
Traffic collision on the highway has become a serious issue because they delay the sustainable development of society. Highway accidents on I-69 have one of the highest crash rates in the U.S. in Michigan and a higher fatality rate than average.
Abdulla I. M. Almadi +5 more
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Traffic collision on the highway has become a serious issue because they delay the sustainable development of society. Highway accidents on I-69 have one of the highest crash rates in the U.S. in Michigan and a higher fatality rate than average.
Abdulla I. M. Almadi +5 more
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IEEE Transactions on Geoscience and Remote Sensing, 2022
Multinomial logistic regression (MLR) is of great significance in hyperspectral image (HSI) classification within remote sensing community. It seeks the optimal regressors with logistic loss function. In the past, spatial information has been widely used
Xiaotao Wang
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Multinomial logistic regression (MLR) is of great significance in hyperspectral image (HSI) classification within remote sensing community. It seeks the optimal regressors with logistic loss function. In the past, spatial information has been widely used
Xiaotao Wang
semanticscholar +1 more source

