Bayesian analysis of binary and polychotomous response data

By Jim Albert, Siddhartha Chib. 1993

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Citations in the corpus

Nonlinear Statistical Learning with Truncated Gaussian Graphical Models (2016, methodology): “In the second, we solve multi-class classiﬁcation by using the multinomial probit link function (Albert & Chib, 1993) to transform the continuous target variables of a TGGM into categorical variables.”
BART: Bayesian Additive Regression Trees (2008, methodology): “Turning to posterior calculation, the essential features of the backﬁtting al-gorithm in Section 3.1 can be implemented by using the augmentation idea of Albert & Chib (1993).”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, background): “A promising alternative is the robit model, which replaces the logistic link by the cumulative distribution function of a Student’s t-distribution with ν degrees of freedom (Albert and Chib, 1993).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, neutral): “A promising alternative is the robit model, which replaces the logistic link by the cumulative distribution function of a Student’s t-distribution with ν degrees of freedom (Albert and Chib, 1993).”
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification (2016, background): “…proposed in the literature, such as the Bradley-Terry model (Bradley & Terry, 1952; Zadrozny, 2001; Menke & Martinez, 2008), the multinomial probit (Albert & Chib, 1993), the spherical softmax (Ollivier, 2013; Vincent, 2015; de Br´ebisson & Vincent, 2015), or softmax approximations (Bouchard,…”
On the Predictive Distributions of Outcome Gains in the Presence of an Unidentified Parameter (2003, background): “that: (1) the posterior distribution of ‰10 will be shifted to the left relative to Table 1A, and ( 2 )”
Determinants of a Digital Divide in Sub-Saharan Africa: A Spatial Econometric Analysis of Cell Phone Coverage (2008, background): “The special advantage of the Gibbs Sampler for use in spatial probit stems from the fact that it also simulates the continuous latent variable (Albert and Chib, 1993).”
Parametric links for binary choice models: A Fisherian-Bayesian colloquy (2009, methodology): “Liu (2004) has previously suggested estimating such models using the EM algorithm, and there have been several Bayesian MCMC proposals following Albert and Chib (1993). We consider estimation and inference in the Gosset link model from both a classical Fisherian maximum likelihood viewpoint and from”
A hierarchical model for ordinal matrix factorization (2011, methodology): “To derive the sampler we introduce the “noise-free” latent variable (see Albert and Chib 1993).”
Program Evaluation as a Decision Problem (2005, unknown): “The Gibbs sampling algorithm for the Tobit model has been worked out by Chib (1992) (see also Albert and Chib, 1993 ).”
A skew item response model (2006, methodology): “Our approach is motivated by the method of Albert and Chib (1993), where the underlying latent variable follows a normal distribution.”
Semiparametric Bayes analysis of longitudinal data treatment models (2002, methodology): “The calculation of these posterior distributions is akin to the way in which Albert and Chib (1995) 0nd the posterior distribution of latent residuals in binary data models.”
The Matrix Stick-Breaking Process (2008, methodology): “Note that this algorithm can easily be adapted to allow categorical ymi using the method of Albert and Chib (1993).”
Measuring the consumer benefits of improving farm animal welfare to inform welfare labelling (2012, methodology): “Posterior estimation was carried out using Gibbs sampling with data augmentation (Albert and Chib, 1993).”
Bayesian Estimation and Model Choice in Item Response Models (2002, methodology): “In the IRT setting: Albert (1992) and Albert and Chib (1993) propose the Gibbs sampler for normal-ogive models; Ghosh et al. (2000) examine integrability of the associated posterior distributions; Patz and Junker (1999a, 1999b) describe and use more advanced MCMC methods.”
The analysis of ordered categorical data: An overview and a survey of recent developments (2005, background): “Like Albert and Chib (1993), Johnson assumed that for a given item, a normal latent variable underlies the categorical rating.”