Zobrazeno 1 - 10
of 267
pro vyhledávání: '"Frühwirth-Schnatter, Sylvia"'
We propose a Bayesian approach for model-based clustering of multivariate categorical data where variables are allowed to be associated within clusters and the number of clusters is unknown. The approach uses a two-layer mixture of finite mixtures mo
Externí odkaz:
http://arxiv.org/abs/2407.05431
Many current approaches to shrinkage within the time-varying parameter framework assume that each state is equipped with only one innovation variance for all time points. Sparsity is then induced by shrinking this variance towards zero. We argue that
Externí odkaz:
http://arxiv.org/abs/2312.10487
In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303-324, 2016) are sparse finite mixtures, where the prior distribution o
Shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying Parameters to staticones, if the model is overfitting. This is achieved through placing the double gamma
Mixtures of factor analysers (MFA) models represent a popular tool for finding structure in data, particularly high-dimensional data. While in most applications the number of clusters, and especially the number of latent factors within clusters, is m
Externí odkaz:
http://arxiv.org/abs/2307.07045
In this paper we study data on discrete labor market transitions from Austria. In particular, we follow the careers of workers who experience a job displacement due to plant closure and observe - over a period of 40 quarters - whether these workers m
Externí odkaz:
http://epub.wu.ac.at/6541/1/euclid.aoas.1536652975.pdf
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphic
Externí odkaz:
http://epub.wu.ac.at/4875/1/research_report_updated.pdf
Generalized Cumulative Shrinkage Process Priors with Applications to Sparse Bayesian Factor Analysis
Autor:
Frühwirth-Schnatter, Sylvia
The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (2020), which is a spike-and-slab shrinkage prior where the spike probabi
Externí odkaz:
http://arxiv.org/abs/2303.00473
The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constra
Externí odkaz:
http://epub.wu.ac.at/5319/1/Identifying_Mixtures_of_Mixtures_Using_Bayesian_Estimation.pdf
There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonity of factor models. In this spirit, we estimate the number of common f
Externí odkaz:
http://arxiv.org/abs/2301.06459