Bernoulli Regression Models: Re-examining Statistical Models with Binary Dependent Variables [PDF]
The classical approach for specifying statistical models with binary dependent variables in econometrics using latent variables or threshold models can leave the model misspecified, resulting in biased and inconsistent estimates as well as erroneous ...
Bergtold, Jason S., Spanos, Aris
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Regularization Paths for Generalized Linear Models via Coordinate Descent [PDF]
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include âÂÂ_1 (the lasso), à ...
Jerome H. Friedman +2 more
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Group Lasso for generalized linear models in high dimension [PDF]
, 2013 Nowadays an increasing amount of data is available and we have to deal with models in high dimension (number of covariates much larger than the sample size).
Blazère, Mélanie +2 more
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Axiomatic Interpretability for Multiclass Additive Models
, 2019 Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable.
Caruana, Rich +5 more
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The Estimation of Item Response Models with the lmer Function from the lme4 Package in R [PDF]
In this paper we elaborate on the potential of the lmer function from the lme4 package in R for item response (IRT) modeling. In line with the package, an IRT framework is described based on generalized linear mixed modeling. The aspects of the framework
Abe Hofman +6 more
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Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model [PDF]
, 2013 We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs.
Calabrese, Raffaella +2 more
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Measurement Error in Lasso: Impact and Correction
, 2014 Regression with the lasso penalty is a popular tool for performing dimension reduction when the number of covariates is large. In many applications of the lasso, like in genomics, covariates are subject to measurement error.
Frigessi, Arnoldo +2 more
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Journal of Mazandaran University of Medical Sciences, 2022 Background and purpose: Controlling the severity and death of the COVID-19 disease is still a major challenge. This research aimed at identifying the factors associated with mortality in hospitalized patients with COVID-19 applying generalized linear model.
Faezeh Sadat Movahedi +4 more
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Design Issues for Generalized Linear Models: A Review
, 2006 Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated.
Ghosh, Malay +3 more
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Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates [PDF]
, 2005 Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL).
Guha, Subharup, Ryan, Louise
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