Results 21 to 30 of about 92 (91)

Nonparametric relative recursive regression

Dependence Modeling, 2020
In this paper, we propose the problem of estimating a regression function recursively based on the minimization of the Mean Squared Relative Error (MSRE), where outlier data are present and the response variable of the model is positive.
Slaoui Yousri, Khardani Salah
doaj   +1 more source

Application of one‐step method to parameter estimation in ODE models

Statistica Neerlandica, Volume 72, Issue 2, Page 126-156, May 2018., 2018
In this paper, we study application of Le Cam's one‐step method to parameter estimation in ordinary differential equation models. This computationally simple technique can serve as an alternative to numerical evaluation of the popular non‐linear least squares estimator, which typically requires the use of a multistep iterative algorithm and repetitive ...
Itai Dattner, Shota Gugushvili
wiley   +1 more source

Analyzing high dimensional correlated data using feature ranking and classifiers

Computational and Mathematical Biophysics, 2019
The Illumina Infinium HumanMethylation27 (Illumina 27K) BeadChip assay is a relatively recent high-throughput technology that allows over 27,000 CpGs to be assayed.
Patil Abhijeet R, Chang Jongwha, Leung Ming-Ying, Kim Sangjin   +3 more
doaj   +1 more source

Goodness-of-Fit Tests in Nonparametric Regression [PDF]

, 2006
AMS classifications: 62G08, 62G10, 62G20, 62G30 ...
John H. J. Einmahl, Einmahl, J.H.J., Ingrid van Keilegom, van Keilegom, I.   +3 more
core   +2 more sources

Thresholding procedure with priors based on Pareto distributions [PDF]

, 2004
adaptive estimation, Bayesian model, Pareto distribution, sparsity, wavelet thresholding, weak Besov spaces, 62G05, 62G08, 62C12,
Rivoirard, Vincent, Vincent Rivoirard
core   +1 more source

A review on consistency and robustness properties of support vector machines for heavy-tailed distributions [PDF]

, 2009
Regularized empirical risk minimization, Support vector machines, Consistency, Robustness, Bouligand influence function, Heavy tails, 68Q32, 62G35, 62G08, 62F35, 68T10,
Arnout Van Messem, Van Messem, Arnout, Andreas Christmann, Christmann, Andreas   +3 more
core   +1 more source

Nonparametric estimation for a stochastic volatility model [PDF]

, 2010
Diffusion coefficient, Drift, Mean square estimator, Model selection, Nonparametric estimation, Penalized contrast, Stochastic volatility, 62G08, 62M05, 62P05, C14, C87,
Comte, Fabienne, V. Genon-Catalot, Rozenholc, Yves, F. Comte, Genon-Catalot, Valentine, Y. Rozenholc   +5 more
core   +1 more source

Bias-variance decomposition in Genetic Programming

Open Mathematics, 2016
We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the
Kowaliw Taras, Doursat René
doaj   +1 more source

Bootstrap tests for nonparametric comparison of regression curves with dependent errors [PDF]

, 2007
Hypothesis testing, Regression models, Nonparametric estimators, Dependent data, 62G08, 62G09, 62G10, 62M10,
W. González-Manteiga, Vilar, Juan M., J. Vilar-Fernández, Vilar, José   +3 more
core   +1 more source

Nonparametric regression on the hyper-sphere with uniform design [PDF]

, 2011
Nonparametric regression, Uniform design, Minimax rate, Needlets, Needlet-shrinkage, Stochastic thresholding, 62G08, 62G05, 62C20,
Monnier, Jean-Baptiste, Jean-Baptiste Monnier   +1 more
core   +1 more source