14,587 research outputs found
Nonparametric Bayes modeling of count processes
Data on count processes arise in a variety of applications, including
longitudinal, spatial and imaging studies measuring count responses. The
literature on statistical models for dependent count data is dominated by
models built from hierarchical Poisson components. The Poisson assumption is
not warranted in many applications, and hierarchical Poisson models make
restrictive assumptions about over-dispersion in marginal distributions. This
article proposes a class of nonparametric Bayes count process models, which are
constructed through rounding real-valued underlying processes. The proposed
class of models accommodates applications in which one observes separate
count-valued functional data for each subject under study. Theoretical results
on large support and posterior consistency are established, and computational
algorithms are developed using Markov chain Monte Carlo. The methods are
evaluated via simulation studies and illustrated through application to
longitudinal tumor counts and asthma inhaler usage
Hybrid approximate message passing
Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to aggregates of many weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (HyGAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance--complexity trade-off can be achieved. Group sparsity and multinomial logistic regression problems are studied as examples of the proposed methodology.The work of S. Rangan was supported in part by the National Science Foundation under Grants 1116589, 1302336, and 1547332, and in part by the industrial affiliates of NYU WIRELESS. The work of A. K. Fletcher was supported in part by the National Science Foundation under Grants 1254204 and 1738286 and in part by the Office of Naval Research under Grant N00014-15-1-2677. The work of V. K. Goyal was supported in part by the National Science Foundation under Grant 1422034. The work of E. Byrne and P. Schniter was supported in part by the National Science Foundation under Grant CCF-1527162. (1116589 - National Science Foundation; 1302336 - National Science Foundation; 1547332 - National Science Foundation; 1254204 - National Science Foundation; 1738286 - National Science Foundation; 1422034 - National Science Foundation; CCF-1527162 - National Science Foundation; NYU WIRELESS; N00014-15-1-2677 - Office of Naval Research
An Extension of Generalized Linear Models to Finite Mixture Outcome Distributions
Finite mixture distributions arise in sampling a heterogeneous population.
Data drawn from such a population will exhibit extra variability relative to
any single subpopulation. Statistical models based on finite mixtures can
assist in the analysis of categorical and count outcomes when standard
generalized linear models (GLMs) cannot adequately account for variability
observed in the data. We propose an extension of GLM where the response is
assumed to follow a finite mixture distribution, while the regression of
interest is linked to the mixture's mean. This approach may be preferred over a
finite mixture of regressions when the population mean is the quantity of
interest; here, only a single regression function must be specified and
interpreted in the analysis. A technical challenge is that the mean of a finite
mixture is a composite parameter which does not appear explicitly in the
density. The proposed model is completely likelihood-based and maintains the
link to the regression through a certain random effects structure. We consider
typical GLM cases where means are either real-valued, constrained to be
positive, or constrained to be on the unit interval. The resulting model is
applied to two example datasets through a Bayesian analysis: one with
success/failure outcomes and one with count outcomes. Supporting the extra
variation is seen to improve residual plots and to appropriately widen
prediction intervals
Stochastic Approximation with Averaging Innovation Applied to Finance
The aim of the paper is to establish a convergence theorem for
multi-dimensional stochastic approximation when the "innovations" satisfy some
"light" averaging properties in the presence of a pathwise Lyapunov function.
These averaging assumptions allow us to unify apparently remote frameworks
where the innovations are simulated (possibly deterministic like in Quasi-Monte
Carlo simulation) or exogenous (like market data) with ergodic properties. We
propose several fields of applications and illustrate our results on five
examples mainly motivated by Finance
Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods
We obtain a Bernstein-type inequality for sums of Banach-valued random
variables satisfying a weak dependence assumption of general type and under
certain smoothness assumptions of the underlying Banach norm. We use this
inequality in order to investigate in the asymptotical regime the error upper
bounds for the broad family of spectral regularization methods for reproducing
kernel decision rules, when trained on a sample coming from a mixing
process.Comment: 39 page
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