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Laplace deconvolution with noisy observations
adaptivity kernel estimation minimax rates Volterra equation
2011/7/19
In the present paper we consider Laplace deconvolution on the basis of discrete noisy data observed on the interval which length may increase with a sample size. Although this problem arises in a vari...
Nonparametric estimation of multivariate extreme-value copulas
empirical copula extreme-value copula Pickands dependence function
2011/7/19
Extreme-value copulas arise in the asymptotic theory for componentwise maxima of independent random samples. An extreme-value copula is determined by its Pickands dependence function, which is a funct...
Riemannian statistics geometry: A counterpart approach of inference geometry
Geometrical Methods in Statistics
2011/7/19
Riemannian statistics geometry is proposed in this work as a counterpart approach of inference geometry.
Loss-sensitive Training of Probabilistic Conditional Random Fields
Loss-sensitive Training Probabilistic Conditional Random Fields
2011/7/19
We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function. While maximum likelihood is th...
Application of Predictive Model Selection to Coupled Models
Predictive Model Selection Quantity of In-terest Model Validation Decision Making
2011/7/19
A predictive Bayesian model selection approach is presented to discriminate coupled models used to predict an unobserved quantity of interest (QoI).
On Low-Dimensional Projections of High-Dimensional Distributions
Low-Dimensional Projections High-Dimensional Distributions
2011/7/19
Let $P$ be a probability distribution on $q$-dimensional space. The so-called Diaconis-Freedman effect means that for a fixed dimension $d << q$, most $d$-dimensional projections of $P$ look like a sc...
KARMA: Kalman-based autoregressive moving average modeling and inference for formant and antiformant tracking
autoregressive moving average modeling inference for formant
2011/7/19
Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using...
The Rate of Convergence of AdaBoost
AdaBoost optimization coordinate descent convergence rate.
2011/7/7
The AdaBoost algorithm was designed to combine many "weak" hypotheses that perform slightly better than random guessing into a "strong" hypothesis that has very low error.
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
Widely Linear vs. Conventional Subspace-Based Estimation of SIMO Flat-Fading Channels: Mean-Squared Error Analysis
Subspace-Based Estimation SIMO Flat-Fading Channels: Mean-Squared Error Analysis
2011/7/7
We analyze the mean-squared error (MSE) performance of widely linear (WL) and conventional subspace-based channel estimation for single-input multiple-output (SIMO) flat-fading channels employing bina...
Covariance Estimation for Distributions with 2+εMoments
Covariance Estimation Distributions 2+εMoments
2011/7/7
We study the minimal sample size N=N(n) that suffices to estimate the covariance matrix of an n-dimensional distribution by the sample covariance matrix in the operator norm, and with an arbitrary fix...
Sequential Monte Carlo (SMC) approaches have become work horses in approximate Bayesian computation (ABC). Here we discuss how to construct the perturbation kernels that are required in ABC-SMC approa...
Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method
Sparse Covariance Estimation Adaptive Gradient-Based Method
2011/7/6
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphi...
Parametric inference and forecasting in continuously invertible volatility models
Invertibility volatility models parametric estimation
2011/7/6
We introduce the notion of continuously invertible volatility models that relies on some Lyapunov condition and some regularity condition.
Finite mixture models with predictive recursion marginal likelihood
Density estimation Dirichlet distribution mixture com-plexity
2011/7/6
Estimation of finite mixture models when the mixing distribution support is unknown is an important and challenging problem. In this paper, a new approach is given based on the recently proposed predi...