Multiple Logistic Regression
Nonparametric bootstrapping for multiple logistic regression model using R
... a regression model is an important way to rep- resent heterogeneity in a ...for multiple logistic regression model associated with Davidson and Hinkley's (1997) “boot” library in ...
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Clinical evaluation of the temporomandibular joint following orthognathic surgery Multiple logistic regression analysis
... methods used. Panula et al. 6 performed their calcula- tions using the Wilcoxon matched pairs signed-ranks test. Westemark et al. 7 compared the younger patients with the older patients using chi-square analysis. In the ...
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Simulation Program to Determine Sample Size and Power for a Multiple Logistic Regression Model with Unspecified Covariate Distributions
... Logistic regression models have been used to determine the association between risk factors and outcomes in various fields, including medical and epidemiological research [1] ...
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Logistic regression analysis in personal loan bankruptcy
... In this research, Binary Logistic Regression was explored to estimate the parameters. To assess the factors contributed to personal loans bankruptcy, we focuses on analysing the four predictors, that is ...
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Churn Analysis in Telecommunication Using Logistic Regression
... In this paper a Churn Analysis has been applied on Telecom data, here the agenda is to know the possible customers that might churn from the service provider. R programing is used for the same this will help give a ...
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Multiple-Trait Mapping of Quantitative Trait Loci After Selective Genotyping Using Logistic Regression
... or regression interval mapping only animals with extreme phenotypes are genotyped (Lander and Botstein 1989; Haley and Knott 1992; (see Lebowitz et ...The regression methods Darvasi and Soller 1992; Muranty ...
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Sparse Logistic Regression with Logical Features
... sparse logistic models with logical features of multivalued ...of regression models such as Poisson regression and Cox proportional hazards ...
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On goodness-of-fit of logistic regression model
... for logistic regression models have been developed recently (Evans and Li ...assumed logistic regression model is embedded in a more general parametric family of models (Prentice ...
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Transfer learning based on logistic regression
... class. Logistic regression delivers lin- ear decision boundaries in feature ...x, logistic regression is applied to a vector Φ(x) whose compo- nents are (in principle) arbitrary functions of ...
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Financial Distress prediction in the Netherlands: An Application of Multiple Discriminant Analysis, Logistic Regression and Neural Network
... Zopounidis (1996) show in their research of 158 published articles that before 1980 the MDA method for predicting bankruptcy was the most popular method in scientific literature. After 1980 many other mathematic models ...
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Predicting Corporate Bankruptcy in Pakistan A Comparative Study of Multiple Discriminant Analysis (MDA) and Logistic Regression
... The case-wise list gives us the list that didn’t fit the model well. We may call these as outliers. There is only three cases falls into this category and this means that the model is reasonably sound. If the ...
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Ordinal logistic regression in epidemiological studies
... ordinal regression is a parametric technique that, by imposing a rigid, more conservative and economical structure on the model, allows the reliability intervals for the parameters to be quantifi ed; this makes it ...
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Purposeful selection of variables in logistic regression
... The purposeful selection process begins by a univariate analysis of each variable. Any variable having a significant univariate test at some arbitrary level is selected as a can- didate for the multivariate analysis. We ...
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Characterization of Differentially Private Logistic Regression
... subspace multiple times with the DPLR model and a privacy parameter values that are resulted from the supervised learning ...perform multiple queries on a subspace and construct a new ϵ-differentially ...
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Logistic Regression on Homomorphic Encrypted Data at Scale
... for logistic re- gression on homomorphic encrypted data, and evaluate our algorithm on real financial data consisting of 422,108 sam- ples over 200 ...the logistic regression training on large en- ...
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Logistic regression analysis on the determinants of stillbirth in Ethiopia
... A multiple logistic model was fitted to the data to test the research hypothesis regarding the relationship be- tween the likelihood that experiencing stillbirth of woman is related with the predictor ...
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An Improved GLMNET for L1-regularized Logistic Regression
... In Section 3, we show that for expensive loss functions, Newton-type methods are more suit- able. A Newton method needs not compute the loss function when finding the Newton direction, which is the most time consuming ...
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Trauma scoring models using logistic regression.
... The Hosmer Lemeshow statistic is one of the main goodness of fit tests used in logistic regression modelling. This test will be evaluated further in this thesis. The value of the HL test like several other ...
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A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models
... traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes ...
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An Application on Multinomial Logistic Regression Model
... The logistic regression model assumes that the categorical response variable has only two values, in general, 1for success and 0 for ...The logistic regression model can be extended to ...
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