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Nonparametric Regression Estimation with Incomplete Data: Minimax Global Convergence Rates and Adaptivity
Adaptivity Besov spaces inhomogeneous data minimax estimation
2011/7/6
We consider the nonparametric regression estimation problem of recovering an unknown response function $f$ on the basis of incomplete data when the design points follow a known density $g$ with a fini...
Consistent Model Selection of Discrete Bayesian Networks from Incomplete Data
Discrete Bayesian Networks Consistent Model Incomplete Data node-variables
2011/6/20
A maximum likelihood based model selection of discrete Bayesian
networks is considered. The model selection is performed through scoring
function S, which, for a given network G and n-sample Dn, is ...
A History of Markov Chain Monte Carlo——Subjective Recollections from Incomplete Data
History Markov Chain Monte Carlo——Subjective Recollections Incomplete Data
2010/4/30
In this note we attempt to trace the history and development of Markov chain
Monte Carlo (MCMC) from its early inception in the late 1940’s through its use today.
We see how the earlier stages of th...
Formal and Informal Model Selection with Incomplete Data
Interval of ignorance linear mixed model missing at random missing not at random multivariate normal sensitivity analysis
2010/4/30
Model selection and assessment with incomplete data pose
challenges in addition to the ones encountered with complete data.
There are two main reasons for this. First, many models describe character...
Self Consistency：A General Recipe for Wavelet Estimation With Irregularly-spaced and/or Incomplete Data
EM algorithm image denoising non-equispaced data missing data multiscale methods non-parametric regression wavelet thresholding
2010/4/26
Inspired by the key principle behind the EMalgorithm, we propose a general methodology for
conducting wavelet estimation with irregularly-spaced data by viewing the data as the observed
portion of a...