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Estimation of False Discovery Proportion with Unknown Dependence
Approximate factor model large-scale multiple testing dependent test statistics un-known covariance matrix false discovery proportion
2013/6/14
Large-scale multiple testing with highly correlated test statistics arises frequently in many scienti?c research. Incorporating correlation information in estimating false discovery proportion has att...
Adaptive quantile estimation in deconvolution with unknown error distribution
Deconvolution Quantile and distribution function Adaptive es-timation Minimax convergence rates Random Fourier multiplier
2013/4/27
We study the problem of quantile estimation in deconvolution with ordinary smooth error distributions. In particular, we focus on the more realistic setup of unknown error distributions. We develop a ...
Bandlimited Signal Reconstruction From the Distribution of Unknown Sampling Locations
Bandlimited Signal Reconstruction the Distribution Unknown Sampling Locations
2013/4/28
We study the reconstruction of bandlimited fields from samples taken at unknown but statistically distributed sampling locations. The setup is motivated by distributed sampling where precise knowledge...
Estimation of a nonnegative location parameter with unknown scale
Concave loss Convex loss Dominance Estimation Generalized Bayes Lower bounded mean,Lloss Minimax Restricted parameter Residual vector Robustness.
2012/9/19
For normal canonical models, and more generally a vast arrayof general spherically symmetric location-scale models with a residual vector, we consider estimatingthe (univariate) location parameter whe...
About the posterior distribution in hidden Markov Models with unknown number of states
Hidden Markov models number of components order selection Bayesian statistics posterior distribution
2012/9/19
In this paper, we investigate the asymptotic behaviour of the posterior distribution in hidden Markov models (HMMs) when using Bayesian methodology. We obtain a general asymptotic result, and give con...
Perfect Simulation for Mixtures with Known and Unknown Number of components
Bounding chains Dirichlet process Gibbs sampling Mixtures Optimization Perfect Sam-pling
2011/3/24
We propose and develop a novel and effective perfect sampling methodology for simulating from posteriors corresponding to mixtures with either known (fixed) or unknown number of components. For the la...
Perfect Simulation for Mixtures with Known and Unknown Number of components
Bounding chains Dirichlet process Gibbs sampling Mixtures Optimization Perfect Sam-pling
2011/3/23
We propose and develop a novel and effective perfect sampling methodology for simulating from posteriors corresponding to mixtures with either known (fixed) or unknown number of components. For the la...
Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty
Reconstruction signals unknown spectra information field theory parameter uncertainty
2010/3/11
The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge
of their power spectra and other parameters. If these are not known a priori, they have to be
measured ...
Deconvolution with unknown error distribution
Deconvolution Fourier transform kernel estimation spectralcut off Sobolev space source condition optimal rate of convergence
2010/4/29
We assume that an additional sample x1, . . . , xm from fx is observed.
Estimators of fX and its derivatives are constructed by using nonparametric
estimators of fY and fx and by applying a spectral...
Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise
Observational noise model forecasting dynamical systems support vector machines consistency
2010/4/30
We consider the problem of forecasting the next (observable)
state of an unknown ergodic dynamical system from a noisy observation
of the present state. Our main result shows, for example, that
sup...
Gaussian model selection with an unknown variance
Model selection penalized criterion AIC FPE BIC AMDL variable selection change-points detection adaptive estimation
2010/4/26
Let Y be a Gaussian vector whose components are independent
with a common unknown variance. We consider the problem of estimating
the mean μ of Y by model selection. More precisely, we start
with a...
Nonparametric denoising Signals of Unknown Local Structure，II：Nonparametric Regression Estimation
Nonparametric denoising adaptive filtering minimax estimation nonparametric regression
2010/3/18
We consider the problem of recovering of continuous multi-dimensional functions f
from the noisy observations over the regular grid m−1Zd, m ∈ N∗. Our focus is at
the adaptive estimation...