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PAC-Bayesian Estimation and Prediction in Sparse Additive Models
Additive models sparsity regression estimation PAC-Bayesian bounds oracle inequality MCMC stochastic search.
2012/9/17
The present paper is about estimation and prediction in high-dimensional additive models under a sparsity assumption (p nparadigm).A PAC-Bayesian strategy is investigated, delivering oracle inequaliti...
Tangent space estimation for smooth embeddings of Riemannian manifolds
Riemannian manifolds tangent space estimation manifold sampling manifold learning Chernoff bounds for sums of random matrices singular value perturbation.
2012/9/17
Numerous dimensionality reduction problems in data analysis involve the recovery of low-dimensional models or the learning of manifolds underlying sets of data. Many manifold learning methods require ...
Sequential Estimation Methods from Inclusion Principle
Sequential Estimation Methods Inclusion Principle
2012/9/17
In this paper, we propose new sequential estimation methodsbased on inclusion principle. The main idea is to reformulate the estimation problems as constructing sequential random intervals and use con...
Re-Weighted l_1 Dynamic Filtering for Time-Varying Sparse Signal Estimation
Re-Weighted Dynamic Filtering Time-Varying Signal Estimation
2012/9/17
Signal estimation from incomplete observations improves as more signal structure can be exploited in the inference process. Classic algorithms (e.g., Kalman filtering) have exploited strong dynamic st...
Two-step estimation of high dimensional additive models
additive model group Lasso penalized least squares.
2012/9/19
This paper investigates the two-step estimation of a high dimensional additive regression model, in which the number of nonparametric additive components is potentially larger than the sample size but...
Let (V,A) be a weighted graph with a finite vertex set V,with a symmetric matrix of nonnegative weightsAand with Laplacian ∆. LetS∗: V ×V 7→ R be a symmetric kernel defined on the vertex s...
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...
A Robust, Fully Adaptive M-estimator for Pointwise Estimation in Heteroscedastic Regression
Adaptation Huber contrast Lepski’s method M-estimation minimax estimation nonparamet-ric regression pointwise estimation robust estimation.
2012/9/19
We introduce a robust and fully adaptive method for pointwise estimation in heteroscedastic regression. We allow for noise and design distributions that are unknown and fulfill very weak assumptions o...
Quarticity and other functionals of volatility: efficient estimation
semimartingale high frequency data volatility estimation central limit theo-rem efficient estimation
2012/9/19
We consider a multidimensional It坥 semimartingale regularly sampled on [0,t] at high frequency 1/∆n, with ∆n going to zero. The goal of this paper is to provide an estimator for the integr...
Maximum Likelihood Estimation of Gaussian Cluster Weighted Models and Relationships with Mixtures of Regression
Cluster-weighted modeling finite mixtures of regression EM-algorithm
2012/9/19
Cluster-weighted modeling (CWM) is a mixture approach for modeling the joint probability of a response variable and a set of explanatory variables. The parame-ters are estimated by means of the expect...
The Scale Invariant Wigner Spectrum Estimation of Gaussian Locally Self-Similar Processes
Locally self-similar circularly symmetric Gaussian processes scale invariant Wigner spectrum (SIWS) optimal estimation time-frequency analysis
2012/9/19
We study locally self-similar processes (LSSPs) in Silverman’s sense. By deriving the minimum mean-square optimal kernel within Cohen’s class counterpart of time-frequency representations, we obtain a...
A Normal Hierarchical Model and Minimum Contrast Estimation for Random Intervals
random intervals Normality hierarchical Choquet functional minimum contrast estimator strong consistency asymptotic normality.
2012/9/19
Many statistical data are imprecise due to factors such as mea-surement errors, computation errors, and lack of information. In such cases, data are better represented by intervals rather thanby singl...
Estimation of Scale and Hurst Parameters of Semi-Selfsimilar Processes
Hurst estimation Discrete self-similarity Fractional Brownian motion Semi-selfsimilar processes Scale parameter.
2012/9/19
The characteristic feature of semi-selfsimilar process is the invariance of its finite dimensional distributions by certain dilation for specific scaling factor. Estimating the scale parameter λand th...
Bayesian Subset Simulation: a kriging-based subset simulation algorithm for the estimation of small probabilities of failure
Computer experiments Sequential design Subset Simulation Probability of failure
2012/9/19
The estimation of small probabilities of failure from computer simulations is a classical problem in engineering, and the Subset Simulation algorithm proposed by Au & Beck (Prob. Eng. Mech., 2001) has...
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Parameter estimatio stochastic Morris-Lecar neuronal mode particle filter methods
2012/9/19
In this paper, we consider the classic measurement error regression scenario in which our independent,or design, variables are observed with several sources of additive noise. We will show that our mo...