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Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model
maximum-likelihood Stochastic Block Model
2011/6/17
The stochastic block model (SBM) is a probabilistic model de-
signed to describe heterogeneous directed and undirected graphs. In this
paper, we address the asymptotic inference on SBM by use of max...
A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures
metabolomics concentration estimation prior information multi component model block updates
2011/6/17
Nuclear Magnetic Resonance (NMR) spectra are widely used in metabolomics to
obtain profiles of metabolites dissolved in biofluids such as cell supernatants. Methods
for estimating metabolite concent...
A Poisson Mixed Model with Nonnormal Random Effect Distribution
Count data Generalized log-gamma distribution Multivariate negative binomial distribution Overdispersion Random-effect models
2011/6/17
We propose in this paper a random intercept Poisson model in which the random effect distribution
is assumed to follow a generalized log-gamma (GLG) distribution. We derive the first two moments
for...
Estimation in autoregressive model with measurement error
autoregressive model markov chain weak dependent deconvolution semi-parametric nonlinear autoregressive model
2011/6/16
Consider an autoregressive model with measurement error: we observe
Zi = Xi+εi, where Xi is a stationary solution of the equation Xi = f0(Xi−1)+ξi. The
regression function f0 is known up to ...
Semiparametric Additive Transformation Model under Current Status Data
B-spline Consistent variance estimation Current status data Efficient estimation Semiparametric transformation models
2011/6/16
We consider the efficient estimation of the semiparametric additive transformation model
with current status data. A wide range of survival models and econometric models can be incorporated
into thi...
Variational Bayes approach for model aggregation in unsupervised classification with Markovian dependency
Model averaging Variational Bayes inference Markov Chain Unsu-pervised classification
2011/6/16
We consider a binary unsupervised classi cation problem where each observation
is associated with an unobserved label that we want to retrieve. More precisely, we
assume that there are two groups of...
A Functional Version of the ARCH Model
ARCH financial data functional time series high-frequency data weak-dependence
2011/6/16
Improvements in data acquisition and processing techniques have lead to an almost continuous
flow of information for financial data. High resolution tick data are available and can be quite convenien...
Deviance Information Criteria for Model Selection in Approximate Bayesian Computation
Approximate Bayesian computation evolutionary genetics statistical
2011/6/16
Approximate Bayesian computation (ABC) is a class of algorithmic
methods in Bayesian inference using statistical summaries and computer
simulations. ABC has become popular in evolutionary genetics a...
Parameter estimation in a spatial unit root autoregressive model
Spatial autoregressive processes unit root models
2011/3/23
Spatial autoregressive model $X_{k,\ell}=\alpha X_{k-1,\ell}+\beta X_{k,\ell-1}+\gamma X_{k-1,\ell-1}+\epsilon_{k,\ell}$ is investigated in the unit root case, that is when the parameters are on the b...
Parameter estimation in a spatial unit root autoregressive model
Spatial autoregressive processes unit root models
2011/3/22
Spatial autoregressive model $X_{k,\ell}=\alpha X_{k-1,\ell}+\beta X_{k,\ell-1}+\gamma X_{k-1,\ell-1}+\epsilon_{k,\ell}$ is investigated in the unit root case, that is when the parameters are on the b...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/24
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/24
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
A Dynamic Spatio-temporal Precipitation Model
Rainfall modeling Space-time model Bayesian hierarchical model Markov chain Monte Carlo (MCMC) method Censoring Gaussian random fi eld
2011/3/23
A spatio-temporal model for precipitation is presented. Modeling the continuous and the discrete part of rainfall together, it is assumed that precipitation has a censored and power-transformed normal...
Phase transition behavior in a cellular automaton model with different initial configurations
dynamical transition
2011/3/25
We investigate the dynamical transition from free-flow to jammed traffic, which is related to the divergence of the relaxation time and susceptibility of the energy dissipation rate $E_d$, in the Nage...
Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameteri...