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Adaptive asymptotically efficient estimation in heteroscedastic nonparametric regression
asymptotic bounds adaptive estimation efficient estimation het-eroscedastic regression nonparametric regression Pinsker’s constant
2010/3/10
The paper deals with asymptotic properties of the adaptive proce-
dure proposed in the author paper, 2007, for estimating an unknown
nonparametric regression. We prove that this procedure is asympto...
Manifold-Based Signal Recovery and Parameter Estimation from Compressive Measurements
Manifolds dimensionality reduction random projections Compressive Sensing spar-sity signal recovery parameter estimation
2010/3/10
A field known as Compressive Sensing (CS) has recently emerged to help address the growing
challenges of capturing and processing high-dimensional signals and data sets. CS exploits the
surprising f...
Consistent order estimation and the local geometry of mixtures
consistent order estimation penalized likelihood likelihood inequalities uniform law of iterated logarithm empirical process theory bracketing entropy
2010/3/10
Consider an i.i.d. sequence of random variables whose distribution f
lies in one of a nested family of models (Mq)q2N,Mq Mq+1. The smallest
index q such that Mq contains f is called the model orde...
Adaptive LASSO-type estimation for ergodic diffusion processes
discretely observed diffusion processes model selection oracle proper-ties random fields stochastic differential equations
2010/3/10
The LASSO is a widely used statistical methodology for simultaneous estimation
and variable selection. In the last years, many authors analyzed this technique from
a theoretical and applied point of...
Adaptive estimation for Hawkes processes：application to genome analysis
Hawkes’ process model selection oracle inequalities data-driven penalty minimaxrisk adaptive estimation
2010/3/18
The aim of this paper is to provide a new method for the detection
of either favored or avoided distances between genomic events
along DNA sequences. These events are modeled by a Hawkes’ process.
...
Estimation error for blind Gaussian time series filtering
Asymptotic Statistics Covariance Estimation Time Series
2010/3/10
In the frame of time series analysis, we compute the quadratic error in the
blind estimation of the projection operator for prediction with infinite past. The
estimation is made using only a single ...
Distilled Sensing:Adaptive Sampling for Sparse Detection and Estimation
Distilled Sensing Adaptive Sampling Sparse Detection Estimation
2010/3/9
Adaptive sampling results in dramatic improvements in the re-
covery of sparse signals in white Gaussian noise. A sequential adap-
tive sampling-and-refinement procedure called distilled sensing (DS...
Distilled Sensing:Adaptive Sampling for Sparse Detection and Estimation
Adaptive sampling model selection multiple testing sequentialdesign sparse recovery
2010/3/9
Adaptive sampling results in dramatic improvements in the re-
covery of sparse signals in white Gaussian noise. A sequential adap-
tive sampling-and-refinement procedure called distilled sensing (DS...
Hidden Regular Variation:Detection and Estimation
Regular variation vague convergence weak convergence spectral measure risk sets
2010/3/9
Hidden regular variation defines a subfamily of distributions satisfying multivariate
regular variation on E = [0,1]d\{(0, 0, · · · , 0)} and models another regular variation on the sub-cone E(2) = E...
The linearity condition and adaptive estimation in single-index regressions
linearity condition adaptive estimation single-index regressions
2010/3/9
We show that under a linearity condition on the distribution of the predictors,
the coefficient vector in a single-index regression can be estimated with
the same efficiency as in the case when the ...
Robust quantile estimation and prediction for spatial processes
Spatial processes Kernel estimate Conditional quantile Spatial prediction
2010/3/9
In this paper, we present a statistical framework for modeling conditional quantiles of spatial processes
assumed to be strongly mixing in space. We establish the L1 consistency and the asymptotic no...
Sparse covariance estimation in heterogeneous samples
Covariance selection Dirichlet process Gaussian graphical model HiddenMarkov model Nonparametric Bayes inference
2010/3/9
Standard Gaussian graphical models (GGMs) implicitly assume that the conditional independence
among variables is common to all observations in the sample. However, in practice,
observations are usua...
Non-Gaussian Quasi Maximum Likelihood Estimation of GARCH Models
Non-Gaussian Quasi Maximum Likelihood Estimation GARCH Models
2010/3/9
The non-Gaussian quasi maximum likelihood estimator is frequently used
in GARCH models with intension to improve the efficiency of the GARCH
parameters. However, the method is usually inconsistent u...
Asymptotic equivalence and sufficiency for volatility estimation under microstructure noise
High-frequency data integrated volatility spot volatilityestimation Le Cam deficiency equivalence of experiments
2010/3/9
The basic model for high-frequency data in finance is considered,
where an efficient price process is observed under microstructure noise.
It is shown that this nonparametric model is in Le Cam’s se...
Asymptotically optimum estimation of a probability in inverse binomial sampling
Asymptotic properties Inverse binomial sampling Sequential es-timation.
2010/3/9
The optimum quality that can be asymptotically achieved in the estimation
of a probability p using inverse binomial sampling is considered in this paper.
A general definition of quality is used, in ...