Ordinal Data Modeling

By Valen E. Johnson, Jim Albert. 2000

529 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0fbb51bc09f88569500f63bcf3f538f74b7703ff

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In Signal Processing

Citations in the corpus

Recovery of Item Parameters in the Nominal Response Model: A Comparison of Marginal Maximum Likelihood Estimation and Markov Chain Monte Carlo Estimation (2002, background): “…) and a transition probability from ω(t) to ω(t+1) of p(ω(t),ω(t+1)) = k∏ l=1 p(ω (t+1) l | (ω(t)j , for j > l), (ω(t+1)j , for j l),Y), j = l. (1) It has been shown that the joint distribution of ω(t) asymptotically converges to the joint posterior distribution, π(ω|Y) (Johnson & Albert, 1999).”
MCMCpack: Markov chain Monte Carlo in R (2011, methodology): “It is well known that WinBUGS has trouble with algorithms that require sampling from truncated distributions , such as the Albert and Chib (1993) algorithm for the binary probit model, and algorithms for fitting ordinal data models (Cowles 1996; Johnson and Albert 1999).”
A skew item response model (2006, methodology): “By considering this latent structure, the full conditionals for the skew-probit IRT model and the Bayesian inference via MCMC follows without complications, similarly as reported in Johnson and Albert (1999).”
Bayesian Nonparametric Modeling for Multivariate Ordinal Regression (2014, methodology): “This is in contrast to the formulation of Johnson and Albert (1999), in which the assumption is made that all judges actually agree on the intrinsic worth of each item, so that where w i represents the true latent score, and (cid:15) ij is the error observed by judge j .”
Enhancing the Validity and Cross-Cultural Comparability of Measurement in Survey Research (2003, unknown): “This generlizes the well-known result in ordered probit that the thresholds are not all separately identified from the constant term (Johnson and Albert 1999, ch. 5).”
The V–Dem Measurement Model: Latent Variable Analysis for Cross-National and Cross-Temporal Expert-Coded Data (2015, unknown): “The first parameterization is typically called multi-rater ordinal probit (MROP) (Johnson & Albert 1999, Pemstein, Meserve & Melton 2010), 8 while the latter is an ordinal item response theory (O-IRT) setup (Clinton & Lewis 2008, Treier & Jackman 2008).”