arXiv 13 Sep 2018 · Econometrics · publishedAdvances in Data Analysis and Classification (2019) · 5 citations (OpenAlex)
arXiv:1809.04853 · PDF · DOI · OpenAlex · Extracted main text
A method for implicit variable selection in mixture of experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Polson, N. G., Scott, J. G., and Windle, J (2013) Bayesian inference for logistic models using pólya–gamma latent variables | 1.000 | 8 | 4 | 100% |
| 2 | Frühwirth-Schnatter, S (2006) Finite mixture and Markov switching models | 1.000 | 5 | 3 | 100% |
| 3 | Griffin, J. E. and Brown, P. J (2010) Inference with normal-gamma prior distributions in regression problems | 0.928 | 4 | 3 | 100% |
| 4 | Gormley, I. C. and Frühwirth-Schnatter, S (2018) Mixture of expert models | 0.811 | 4 | 2 | 100% |
| 5 | Frühwirth-Schnatter, S (2004) Estimating marginal likelihoods for mixture and markov switching models using bridge sampling techniques | 0.737 | 5 | 2 | 60% |
| 6 | George, E. I. and McCulloch, R. E (1993) Variable selection via gibbs sampling | 0.737 | 3 | 2 | 100% |
| 7 | Ghosh, J., Herring, A. H., and Siega-Riz, A. M (2011) Bayesian variable selection for latent class models | 0.737 | 3 | 2 | 100% |
| 8 | Malsiner-Walli, G., Frühwirth-Schnatter, S., and Grün, B (2016) Model-based clustering based on sparse finite gaussian mixtures | 0.737 | 3 | 2 | 100% |
| 9 | Bitto, A. and Frühwirth-Schnatter, S (2018) Achieving shrinkage in a time-varying parameter model framework | 0.644 | 3 | 2 | 67% |
| 10 | Lenk, P. J. and DeSarbo, W. S (2000) Bayesian inference for finite mixtures of generalized linear models with random effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 61 scored citations.