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Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/20
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
Asymptotic Equivalence of Spectral Density Estimation and Gaussian White Noise
Stationary Gaussian process spectral density Sobolev classes Le Cam distance asymptotic equivalence Whittle likelihood log-periodogram regression nonparametric Gaussian scale model signal in Gaussian white noise
2015/8/25
We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f. Asymptotic equivalence, in the sense of Le Cam’s...
ASYMPTOTIC EQUIVALENCE OF SPECTRAL DENSITY ESTIMATION AND GAUSSIAN WHITE NOISE
ASYMPTOTIC EQUIVALENCE SPECTRAL DENSITY ESTIMATION GAUSSIAN WHITE NOISE
2015/8/25
We consider the statistical experiment given by a sample y(1), . . . , y(n) of a stationary Gaussian process with an unknown smooth spectral density f . Asymptotic equivalence, in the sense of Le Cam’...
Equivalence Asymptotique des Experiences Statistiques
Equivalence Asymptotique Experiences Statistiques
2015/8/25
The idea of approximating a sequence of statistical experiments by a gaussian family goes back to Wald (1943), but has been fully developed by Lucien Le Cam, who introduced the term "local asymptotic ...
ASYMPTOTIC EQUIVALENCE FOR NONPARAMETRIC REGRESSION
ASYMPTOTIC EQUIVALENCE NONPARAMETRIC REGRESSION
2015/8/25
We consider a nonparametric model En, generated by independent observations Xi, i = 1, ..., n, with densities p(x, θi), i = 1, ..., n, the parameters of which θi = f(i/n) ∈ Θ are driven by the values ...
ASYMPTOTIC EQUIVALENCE OF ESTIMATING A POISSON INTENSITY AND A POSITIVE DIFFUSION DRIFT
ASYMPTOTIC EQUIVALENCE ESTIMATING A POISSON INTENSITY POSITIVE DIFFUSION DRIFT
2015/8/25
We consider a diffusion model of small variance type with positive drift density varying in a nonparametric set. We investigate Gaussian and Poisson approximations to this model in the sense of asympt...
Asymptotic equivalence for nonparametric generalized linear models
Nonparametric regression Statistical experiment De® - ciency distance Global white noise approximation Exponential family Variance stabilizing transformation
2015/8/25
We establish that a non-Gaussian nonparametric regression model is asymptotically equivalent to a regression model with Gaussian noise. The approximation is in the sense of Le Cam's de®- ciency d...
ASYMPTOTIC EQUIVALENCE OF DENSITY ESTIMATION AND GAUSSIAN WHITE NOISE
ASYMPTOTIC EQUIVALENCE DENSITY ESTIMATION GAUSSIAN WHITE NOISE
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
Asymptotic Equivalence of Density Estimation and Gaussian White Noise
Asymptotic Equivalence Density Estimation Gaussian White Noise
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markovchain Markov equivalence class.
2012/11/23
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
Some simulation results of Markov equivalence classes
Mathematical statistics Graphical model causal network space of graphs sparse graph
2011/11/9
Directed acyclic graphs (DAGs) and completed partial directed acyclic graphs (CPDAG) are widely used to represent causal systems or uncertain probability systems. Some studies about small graphs with ...
In this paper we describe General Covariance Union (GCU) and show that solutions to GCU and the Minimum Enclosing Ellipsoid (MEE) problems are equivalent.