706 research outputs found
A Simple Class of Bayesian Nonparametric Autoregression Models
We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.MIUR 2008MK3AFZFONDECYT 1100010NIH/NCI R01CA075981Mathematic
Bayesian functional emulation of CO2 emissions on future climate change scenarios
We propose a statistical emulator for a climate-economy deterministic
integrated assessmentmodel ensemble, based on a functional regression framework.
Inference on the unknown parameters is carried out through a mixed
effects hierarchical model using a fully Bayesian framework with a prior distribution
on the vector of all parameters. We also suggest an autoregressive
parameterization of the covariance matrix of the error, with matching marginal
prior. In this way, we allow for a functional framework for the discretized output
of the simulators that allows their time continuous evaluation
Gender inequalities at work in Southern Europe
Despite a long-term trend towards reduction, the gender gap in employment keeps standing in Southern Europe. Numerous potential causes have been individuated, such as the household configuration, women’s human capital, or the institutions that regulate the labour market. Less is known about the role of the locality. This paper explores what covariates influence women’s access to labour markets, and whether it is unevenly distributed across different countries and regions in Southern Europe. The analysis is based on the dataset round 9 (2018) from the European Social Survey. We focus on the following countries available in the dataset: Cyprus, Italy, Spain and Portugal. Italy and Spain are further differentiated into vulnerable and affluent regions according to the regional GDP in 2018. We apply a regression model for the binary response that is the indicator of having been doing paid work for the last 7 days of each individual in the sample. We adopt the Bayesian approach, to derive conclusions via a whole probability distribution, i.e., the posterior of all parameters, given data. The statistical goal is the selection of the most important covariates for access to the labour market, focusing on gender differences. Our analysis finds out that individual characteristics are mediated by household composition. Even though higher education increases women’s employment, the presence of children and having an employed partner reduce such involvement. Moreover, a larger gender gap is detected in vulnerable regions rather than affluent ones, especially in Italy
A class of measure-valued Markov chains and Bayesian nonparametrics
Measure-valued Markov chains have raised interest in Bayesian nonparametrics
since the seminal paper by (Math. Proc. Cambridge Philos. Soc. 105 (1989)
579--585) where a Markov chain having the law of the Dirichlet process as
unique invariant measure has been introduced. In the present paper, we propose
and investigate a new class of measure-valued Markov chains defined via
exchangeable sequences of random variables. Asymptotic properties for this new
class are derived and applications related to Bayesian nonparametric mixture
modeling, and to a generalization of the Markov chain proposed by (Math. Proc.
Cambridge Philos. Soc. 105 (1989) 579--585), are discussed. These results and
their applications highlight once again the interplay between Bayesian
nonparametrics and the theory of measure-valued Markov chains.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ356 the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
The semi-hierarchical Dirichlet Process and its application to clustering homogeneous distributions
Assessing homogeneity of distributions is an old problem that has received
considerable attention, especially in the nonparametric Bayesian literature. To
this effect, we propose the semi-hierarchical Dirichlet process, a novel
hierarchical prior that extends the hierarchical Dirichlet process of Teh et
al. (2006) and that avoids the degeneracy issues of nested processes recently
described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer
to the homogeneity question and embed the proposed prior in a random partition
model; this procedure allows us to give a more comprehensive response to the
above question and in fact find groups of populations that are internally
homogeneous when I greater or equal than 2 such populations are considered. We
study theoretical properties of the semi-hierarchical Dirichlet process and of
the Bayes factor for the homogeneity test when I = 2. Extensive simulation
studies and applications to educational data are also discussed
Spatially dependent mixture models via the Logistic Multivariate CAR prior
We consider the problem of spatially dependent areal data, where for each
area independent observations are available, and propose to model the density
of each area through a finite mixture of Gaussian distributions. The spatial
dependence is introduced via a novel joint distribution for a collection of
vectors in the simplex, that we term logisticMCAR. We show that salient
features of the logisticMCAR distribution can be described analytically, and
that a suitable augmentation scheme based on the P\'olya-Gamma identity allows
to derive an efficient Markov Chain Monte Carlo algorithm. When compared to
competitors, our model has proved to better estimate densities in different
(disconnected) areal locations when they have different characteristics. We
discuss an application on a real dataset of Airbnb listings in the city of
Amsterdam, also showing how to easily incorporate for additional covariate
information in the model
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