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Greedy algorithms for prediction (Q265302)

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Language	Label	Description	Also known as
English	Greedy algorithms for prediction	scientific article

Statements

scholarly article

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Greedy algorithms for prediction (English)

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Alessio Sancetta

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publication date

1 April 2016

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full work available at URL

https://arxiv.org/abs/1602.01951

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https://projecteuclid.org/euclid.bj/1447077774

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zbMATH Keywords

boosting

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forecast

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Frank-Wolfe algorithm

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Hilbert space projection

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regression function

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MaRDI profile type

MaRDI publication profile

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Non-strong mixing autoregressive processes

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A dynamic model of expected bond returns: A functional gradient descent approach

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Splines for Financial Volatility

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Universal approximation bounds for superpositions of a sigmoidal function

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Approximation and learning by greedy algorithms

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\(\ell _{1}\)-regularized linear regression: persistence and oracle inequalities

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Regular variation of GARCH processes.

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Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

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\(\ell_1\)-penalized quantile regression in high-dimensional sparse models

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A new mixing notion and functional central limit theorems for a sieve bootstrap in time series

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Gaussian model selection

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Basic properties of strong mixing conditions. A survey and some open questions

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Boosting for high-dimensional linear models

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Statistical significance in high-dimensional linear models

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Statistics for high-dimensional data. Methods, theory and applications.

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Boosting With the<i>L</i><sub>2</sub>Loss

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Sparsity oracle inequalities for the Lasso

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Aggregation for Gaussian regression

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A cross-validatory method for dependent data

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DATA-DEPENDENT ESTIMATION OF PREDICTION FUNCTIONS

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Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise

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The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).

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Sieve Extremum Estimates for Weakly Dependent Data

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Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm

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An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

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A new covariance inequality and applications.

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Some remarks on greedy algorithms

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Minimax estimation via wavelet shrinkage

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A new weak dependence condition and applications to moment inequalities

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Invariance principles for absolutely regular empirical processes

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Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation

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Least angle regression. (With discussion)

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Pathwise coordinate optimization

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Best subset selection, persistence in high-dimensional statistical learning and optimization under \(l_1\) constraint

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Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization

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Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion

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A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training

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Two lower estimates in greedy approximation

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High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity

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Matching pursuits with time-frequency dictionaries

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Mixing properties of ARMA processes

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Propriétés de mélange des processus autorégressifs polynomiaux. (Mixing properties of polynomial autoregressive processes)

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Confidence sets in sparse regression

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A maximal 𝕃_{𝕡}-inequality for stationary sequences and its applications

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Forecasting Time Series Subject to Multiple Structural Breaks

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Forecast Combination Across Estimation Windows

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Maximal inequalities via bracketing with adaptive truncation

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Risk bounds for mixture density estimation

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Asymptotic theory of weakly dependent stochastic processes

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Bootstrap model selection for possibly dependent and heterogeneous data

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A Recursive Algorithm for Mixture of Densities Estimation

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A NONPARAMETRIC ESTIMATOR FOR THE COVARIANCE FUNCTION OF FUNCTIONAL DATA

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Greedy Approximation

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Weak greedy algorithms

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Degrees of freedom in lasso problems

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Convergence of a block coordinate descent method for nondifferentiable minimization

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Learning Theory and Kernel Machines

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Introduction to nonparametric estimation

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On the uniform convergence of empirical norms and inner products, with application to causal inference

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On asymptotically optimal confidence regions and tests for high-dimensional models

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High-dimensional generalized linear models and the lasso

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On Measuring and Correcting the Effects of Data Mining and Model Selection

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Nearly unbiased variable selection under minimax concave penalty

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On the ``degrees of freedom'' of the lasso

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Identifiers

zbMATH Open document ID

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10.3150/14-BEJ691

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

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