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High Dimensional Stochastic Regression with Latent Factors, Endogeneity and Nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/26
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors, a linear com-bination of some ...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing dimension reduction instrument variables nonstationarity time series
2016/1/25
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
High dimensional stochastic regression with latent factors, endogeneity and nonlinearity
α-mixing, dimension reduction instrument variables nonstationarity time series
2016/1/20
We consider a multivariate time series model which represents a high dimensional vector process as a sum of three terms: a linear regression of some observed regressors,a linear combination of some la...
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
LDHMMs sequential data variational inference variational EM behavior modeling sequence classification
2013/6/14
This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each sequence is generat...
Out-of-sample Extension for Latent Position Graphs
out-of-sample extension inhomogeneous random graphs latent position model convergence of eigenvectors
2013/6/14
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some Euclidean space foll...
MCMC for non-linear state space models using ensembles of latent sequences
MCMC non-linear state space models ensembles latent sequences
2013/6/13
Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no str...
ParceLiNGAM: A causal ordering method robust against latent confounders
ParceLiNGAM A causal ordering method robust against latent confounders
2013/4/28
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming t...
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
DAG maximal ancestral graph Markov equivalence
2012/9/18
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one repre...
Assessing the Health of Richibucto Estuary with the Latent Health Factor Index
AMBI Bayesian statistics hierarchical modelling infaunal trophic index Markov chain Monte Carlo statistical inference
2012/9/17
The ability to quantitatively assess the health of an ecosystem is often of great interest to those tasked with monitoring and conserving ecosystems. For decades, research in this area has relied upon...
Dealing with nonresponse in survey sampling: a latent modeling approach
unit nonresponse item nonresponse latent trait models response propensity non-ignorable nonresponse
2012/9/19
Nonresponse is present in almost all surveys and can severely bias estimates. It is usually distinguished between unit and item nonresponse: in the former, we completely fail to have information from ...
Spectral Methods for Learning Multivariate Latent Tree Structure
Spectral Methods Learning Multivariate Latent Tree Structure
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Spectral Methods for Learning Multivariate Latent Tree Structure
Multivariate Latent Spectral Methods
2011/7/19
This work considers the problem of learning the structure of a broad class of multivariate latent variable tree models, which include a variety of continuous and discrete models (including the widely ...
Linear Latent Force Models using Gaussian Processes
Gaussian Processes Linear Latent Force Models
2011/7/19
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
Estimation of latent variable models for ordinal data via fully exponential Laplace approximation
GLLVM Bartholomew response pattern parameters latent variable models Laplace
2011/6/17
Latent variable models represent a useful tool in the social sciences where the analyzed
constructs cannot be directly observed and, hence, they are not measurable. However, a set
of indicators rela...
Semiparametric Latent Variable Models for Guided Representation
Variable Models Semiparametric Latent Guided
2011/3/18
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for learning features re...