logistic regression based models
Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
... different logistic regression models. These models all contained exactly the same set of linguistic predictors but differed in the estimates used for referent type surprisal and residual ...
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A simulation study of sample size for multilevel logistic regression models
... multilevel regression models as reported by other researchers ...multilevel logistic regression models see Austin ...are based on extensive simulation studies from data which are ...
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Attribution of Customers’ Actions Based on Machine Learning Approach
... accurate models based on machine ...Bagged Logistic Regression, Hidden Markov Chains, Shapley value approach, Survival Analysis, relative weights, and probabilistic ...method based on ...
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A nonparametric multiple imputation approach for missing categorical data
... calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) ...
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LTL UDE at SemEval 2019 Task 6: BERT and Two Vote Classification for Categorizing Offensiveness
... For Subtask A, we experiment with word list- based classification, using classifiers such as SVM or logistic regression based on sentence embed- dings, and neural network-based models su[r] ...
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A Predictive Model for Graduate Application to Enrollment
... used logistic regression to develop predictive models for forecasting enrollment, logistic regression is not appropriate for predicting graduate enrollment because not all applicants ...
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A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients
... these models was chosen to maximize the model prediction accuracy and generalizability since only a limited number of vari- ables were used [53, ...54]. Logistic regression based methods were ...
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Logistic regression applied to natural hazards: rare event logistic regression with replications
... various models (Be- guer´ıa, 2006b). Multivariate statistical models are frequently used for landslide susceptibility analyses, and a classification threshold or so-called cut-off value is often selected to ...
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Computational Algorithms for Penalized Logistic Regression with Categorical Predictors and Random Effect Logistic Models.
... In statistical modeling, variable selection plays an important part: the initial model may include irrelevant covariates, while variable selection can detect and eliminate the in- significant predictors and thus lead to ...
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Maximum Expected F Measure Training of Logistic Regression Models
... tic regression models for binary classifi- cation in information extraction and infor- mation retrieval ...tic models for use with such tasks should take into account the demands of the task- ...
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1 Models of forecasting in financial analysis of
... Bankruptcy prediction models based on the logistic regression for companies in 223. the Czech Republic[r] ...
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Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression
... final logistic regression models are summarized in Additional file 2: Table ...Both models use the same set of final variables, however the odds ratios are larger for the model trained on ...
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A logistic regression approach to content based mammogram retrieval
... develops logistic regression models to generalize the 2-class problem and provide an estimate of probability of class ...the regression curve and compute the maxi- mum likelihood. After ...
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Logistic Regression Models to Forecast Travelling Behaviour in Tripoli City
... formed based on the research questions and the ...selected based on number of Tripoli ...and logistic regression method was used in this ...
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[I955.Ebook] Ebook Download Regression Modeling Strategies With Applications To Linear Models Logistic Regression And Survival Analysis Springer Series In Statisti.pdf
... Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes ...
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Trauma scoring models using logistic regression.
... ICISS models could be improved using the three elements of the RTS as separate independent predictor ...ISS based models combined with coded RTS variables have not been fully ...
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Contributions to Statistical Methods for Functional Data Analysis and Generalized Additive Model.
... functional regression, classification of functional data is an important and challenging problem within functional data ...are logistic regression, Fisher’s linear discriminant analysis, and support ...
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London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure
... multivariate-regression models were compared on the Malawi data: linear regression, binary logistic regression with a cut point at the median “Log med” or at an LMUP score of nine “Log ...
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Determining breast cancer histological grade from RNA sequencing data
... different models to ensure accurate model ...empirically based on the outer CV loop training set in the inner CV (10 × tenfold ...chosen based on minimising the average misclassification ...
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Multivariate Analysis of the Fresh State Parameters of Self-Consolidating Concrete
... Initial models that included all variables and interactions were iteratively simplified by discarding terms that were identified as non ...final models for LV1, LV2 and LV3 showed the best fit to the ...
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