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- Author Andrew Gelman, Donald B. Rubin, +13 authors Luke Tierney
- 2010

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. Institute of… (More)

authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King's ecological inference model. The new approach reveals some features of the data that King's approach does not, can be easily generalized to more complicated problems such… (More)

- Wenxin Jiang, Martin A. Tanner
- Neural Networks
- 1999

|In mixtures-of-experts (ME) models, \experts" of generalized linear models are combined, according to a set of local weights called the \gating function". The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. Under certain… (More)

- Alexandre X. Carvalho, Martin A. Tanner
- IEEE Transactions on Neural Networks
- 2005

We consider a class of nonlinear models based on mixtures of local autoregressive time series. At any given time point, we have a certain number of linear models, denoted as experts, where the vector of covariates may include lags of the dependent variable. Additionally, we assume the existence of a latent multinomial variable, whose distribution depends on… (More)

- Wenxin Jiang, Martin A. Tanner
- IEEE Trans. Information Theory
- 2000

| In the class of hierarchical mixtures-of-experts (HME) models, \experts" in the exponential family with generalized linear mean functions of the form (+ x T) are mixed, according to a set of local weights called the \gating functions" depending on the predictor x. Here () is the inverse link function. We provide regularity conditions on the experts and on… (More)

- Wenxin Jiang, Martin A. Tanner
- Neural Computation
- 1999

We investigate a class of hierarchical mixtures-of-experts (HME) models where generalized linear models with nonlinear mean functions of the form psi (alpha + xT beta) are mixed. Here psi (.) is the inverse link function. It is shown that mixtures of such mean functions can approximate a class of smooth functions of the form psi (h(x)), where h(.) epsilon… (More)

- M A Tanner, T R Cech
- Science
- 1997

Self-splicing group I introns, like other large catalytic RNAs, contain structural domains. Although the crystal structure of one of these domains has been determined by x-ray analysis, its connection to the other major domain that contains the guanosine-binding site has not been known. Site-directed mutagenesis and kinetic analysis of RNA splicing were… (More)

- R C Case, Wenxin Jiang, +4 authors Ethan Katz
- 2000

In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by King, Rosen and Tanner (1999) from the 2 2 case to the R C case. As in the 2 2 case, the inferential procedure employs Markov chain… (More)

- Martin A. Tanner
- 2008

In the popular approach of " Bayesian variable selection " (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of… (More)

Machine classiication of acoustic waveforms as speech events is often diicult due to context-dependencies. A vowel recognition task with multiple speakers is studied in this paper via the use of a class of modular and hierarchical systems referred to as mixtures-of-experts and hierarchical mixtures-of-experts models. The statistical model underlying the… (More)