- Referenced in 277 articles
- S/S- PLUS language. Code for regression has been distributed in the R package gss freely...
- Referenced in 64 articles
- space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex ... characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered...
- Referenced in 50 articles
- least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional...
- Referenced in 119 articles
- invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss...
- Referenced in 21 articles
- package brms. brms: Bayesian Regression Models using Stan. Fit Bayesian generalized (non-)linear multilevel models ... full Bayesian inference. A wide range of distributions and link functions are supported, allowing users ... predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users...
- Referenced in 37 articles
- package betareg: Beta Regression. Beta regression for modeling beta-distributed dependent variables, e.g., rates ... maximum likelihood regression (for both mean and precision of a beta-distributed response), bias-corrected ... mixture models and recursive partitioning for beta regressions are provided...
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- Beyond). Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters...
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- implements a multivariate model involving a multiple regression sequence where imputation is carried out variable ... generating draws from the predictive distribution specified by the regression model. An iterative imputation scheme...
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- functions, and discuss the results for distributed ridge regression, logistic regression and binary classification with...
- Referenced in 25 articles
- logistic and Poisson regression model with random effects whose distribution is specified as a penalized...
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- current emphasis is on conformal prediction in regression. Soon, we will add tools for density ... examples in the paper ”Distribution-Free Predictive Inference for Regression”. This R code relies ... package. Main reference: ”Distribution-Free Predictive Inference for Regression” by Jing Lei, Max G’Sell...
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- multiple imputation and Haley-Knott regression. R/qtl is distributed as source code for unix...
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- Approximate Bayesian Computation with Kernel-Based Distribution Regression. Performing exact posterior inference in complex generative ... this task using kernel-based distribution regression. We model the functional relationship between data distributions ... summary statistics using kernel-based distribution regression. We show that our approach can be implemented...
- Referenced in 13 articles
- regression that provides a bridge between ridge regression and the lasso. The estimate that ... Bayesian posterior mode under a prior distribution implied by the form of the elastic ... characterizations of the class of prior distributions are introduced: a properly normalized, direct characterization, which ... linear regression models, and an alternate representation as a scale mixture of normal distributions. Prior...
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- estimate linear regression models when we assume the random errors distributed according to an exponential...
- Referenced in 27 articles
- well as distributed and decentralized consensus problems. Numerical experiments solving sparse logistic regression problems...
- Referenced in 44 articles
- class of Pólya-Gamma distributions, which are constructed in detail. A variety of examples ... versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effect models ... efficient sampler for the Pólya-Gamma distribution, are implemented in the R package BayesLogit. Supplementary...
- Referenced in 18 articles
- density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables...
- Referenced in 29 articles
- values and the parameters of the null distribution are adaptively estimated from the data ... parametric density estimation (Grenander estimator), for monotone regression (isotonic regression and antitonic regression with weights ... half-normal and correlation distributions, and for computing empirical higher criticism (HC) scores...
- Referenced in 6 articles
- Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal...