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Logistic Regression
This chapter covers a type of generalized linear model, logistic regression, that is applied to settings in which the outcome variable is not...
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Logistic Regression
This chapter covers logistic regression, which is a widely used method in analytics projects for predicting binary outcomes. The chapter begins by...
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The Log-Logistic Regression Model Under Censoring Scheme
A regression model, based on the well-known log-logistic distribution is proposed. This model is parameterized in terms of the median of the...
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Logistic Regression
Logistic regression is an algorithm for classification in two classes. We discuss the interpretation of the coefficients, prediction, and estimation....
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Unit Log-Logistic Distribution and Unit Log-Logistic Regression Model
In this paper, the unit log-logistic distribution was proposed. This distribution is obtained through by transformation of a random variable with...
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A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field
Logistic regression is commonly used to estimate the association of one (or more) independent variable(s) with a binary- dependent outcome. In many...
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Normal-Power-Logistic Distribution: Properties and Application in Generalized Linear Model
The applications of Normal distribution in literature are verse, the new modified univariate normal power distribution is a new distribution which is...
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Robust functional logistic regression
Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables....
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Robust and sparse logistic regression
Logistic regression is one of the most popular statistical techniques for solving (binary) classification problems in various applications (e.g....
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On First Order Autoregressive Asymmetric Logistic Process
An additive first order autoregressive model with logistic distribution as marginal is studied. We have obtained a representation of the innovation...
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Controlling Separation in Generating Samples for Logistic Regression Models
AbstractSeparation has a significant impact on parameter estimates for logistic regression models in frequentist approach and in Bayesian approach....
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Logistic Normal Multinomial Factor Analyzers for Clustering Microbiome Data
The human microbiome plays an important role in human health and disease status. Next-generating sequencing technologies allow for quantifying the...
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A new algorithm and a discussion about visualization for logistic reduced rank regression
Logistic reduced rank regression is a useful data analysis tool when we have multiple binary response variables and a set of predictors. In this...
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FDR control and power analysis for high-dimensional logistic regression via StabKoff
Identifying significant variables for the high-dimensional logistic regression model is a fundamental problem in modern statistics and machine...
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A Random-Effects Regression Model Based on the Odd Log-Logistic Skew Normal Distribution
Regression models with random effects are quite common when the data have a longitudinal or grouped structure for more flexible estimation of the...
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Multinomial Principal Component Logistic Regression on Shape Data
This paper proposes a linear model that uses the principal component scores in shape data and fits the nominal responses in the tangent space of...
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A Discrete Version of the Half-Logistic Distribution Based on the Mimicking of the Probability Density Function
We introduce a count distribution obtained as a discrete analogue of the continuous half-logistic distribution. It is derived by assigning to each...
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On functional logistic regression: some conceptual issues
The main ideas behind the classic multivariate logistic regression model make sense when translated to the functional setting, where the explanatory...
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Logistic regression models for elastic shape of curves based on tangent representations
Shape analysis is widely used in many application areas such as computer vision, medical and biological studies. One challenge to analyze the shape...
