Modeling Farm Equipment Vehicle crash injury severity using random parameters logit and multi-class support vector machine in a developing country. [PDF]
Sci RepNasab EJ +3 more
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Urban welfare and family-oriented settlement intentions of China's floating population. [PDF]
Cost Eff Resour AllocYang W, Zhu X, Xia Y.
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Pose-Driven Cow Behavior Recognition in Complex Barn Environments: A Method Combining Knowledge Distillation and Deployment Optimization. [PDF]
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Machine learning-based prediction models for noninvasive respiratory support failure in acute respiratory failure: a systematic review and meta-analysis. [PDF]
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Comparison of Interpretable Machine Learning Models Using Systemic Inflammation Index to Predict Preterm Birth in Gestational Diabetes Mellitus. [PDF]
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Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches.
Mengyang Li 0001 +3 more
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SSRN Electronic Journal, 2013 AbstractWe model retail price stickiness as the result of costly, error‐prone decision making. Under our assumed cost function for the precision of choice, the timing of price adjustments and the prices firms set are both logit random variables. Errors in the prices firms set help explain micro facts related to the size of price changes, the behavior ...
Anton Nakov, James Costain
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CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS
Advances in Adaptive Data Analysis, 2011For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations.
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This paper reviews aspects of the application of logit models in economics. We consider some economic models that lead to a simple or a multinomial logit specification. A detailed account is given of the possible specifications of the multinomial model. We stress the relationship between distributional assumptions and functional form assumptions. Some (
Cramer, J. S., Ridder, G.
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François Gilbert +2 more
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