TY - RPRT T1 - Bayesian mixture modeling for multivariate conditional distributions Y1 - 2016 A1 - Maria DeYoreo A1 - Jerome P. Reiter AB - We present a Bayesian mixture model for estimating the joint distribution of mixed ordinal, nominal, and continuous data conditional on a set of fixed variables. The model uses multivariate normal and categorical mixture kernels for the random variables. It induces dependence between the random and fixed variables through the means of the multivariate normal mixture kernels and via a truncated local Dirichlet process. The latter encourages observations with similar values of the fixed variables to share mixture components. Using a simulation of data fusion, we illustrate that the model can estimate underlying relationships in the data and the distributions of the missing values more accurately than a mixture model applied to the random and fixed variables jointly. We use the model to analyze consumers' reading behaviors using a quota sample, i.e., a sample where the empirical distribution of some variables is fixed by design and so should not be modeled as random, conducted by the book publisher HarperCollins. PB - ArXiv UR - http://arxiv.org/abs/1606.04457 ER - TY - RPRT T1 - A Bayesian nonparametric Markovian model for nonstationary time series Y1 - 2016 A1 - Maria DeYoreo A1 - Athanasios Kottas AB - Stationary time series models built from parametric distributions are, in general, limited in scope due to the assumptions imposed on the residual distribution and autoregression relationship. We present a modeling approach for univariate time series data, which makes no assumptions of stationarity, and can accommodate complex dynamics and capture nonstandard distributions. The model for the transition density arises from the conditional distribution implied by a Bayesian nonparametric mixture of bivariate normals. This implies a flexible autoregressive form for the conditional transition density, defining a time-homogeneous, nonstationary, Markovian model for real-valued data indexed in discrete-time. To obtain a more computationally tractable algorithm for posterior inference, we utilize a square-root-free Cholesky decomposition of the mixture kernel covariance matrix. Results from simulated data suggest the model is able to recover challenging transition and predictive densities. We also illustrate the model on time intervals between eruptions of the Old Faithful geyser. Extensions to accommodate higher order structure and to develop a state-space model are also discussed. PB - ArXiv UR - http://arxiv.org/abs/1601.04331 ER - TY - RPRT T1 - Categorical data fusion using auxiliary information Y1 - 2015 A1 - Fosdick, B. K. A1 - Maria DeYoreo A1 - J. P. Reiter AB - In data fusion analysts seek to combine information from two databases comprised of disjoint sets of individuals, in which some variables appear in both databases and other variables appear in only one database. Most data fusion techniques rely on variants of conditional independence assumptions. When inappropriate, these assumptions can result in unreliable inferences. We propose a data fusion technique that allows analysts to easily incorporate auxiliary information on the dependence structure of variables not observed jointly; we refer to this auxiliary information as glue. With this technique, we fuse two marketing surveys from the book publisher HarperCollins using glue from the online, rapid-response polling company CivicScience. The fused data enable estimation of associations between people's preferences for authors and for learning about new books. The analysis also serves as a case study on the potential for using online surveys to aid data fusion. PB - arXiv UR - http://arxiv.org/abs/1506.05886 ER -