Results 31 to 40 of about 233 (155)

Upper bounds and aggregation in bipartite ranking

, 2013
One main focus of learning theory is to find optimal rates of convergence. In classification, it is possible to obtain optimal fast rates (faster than n−1/2) in a minimax sense. Moreover, using an aggregation procedure, the algorithms are adaptive to the
Sylvain Robbiano
semanticscholar   +1 more source

Uniform in bandwidth consistency of kernel estimators of the density of mixed data

, 2015
We establish a general uniform in bandwidth consistency result for kernel estimators of the unconditional and conditional joint density of a distribution, which is defined by a mixed discrete and continuous random variable.
D. Mason, J. Swanepoel
semanticscholar   +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

Pricing Bermudan options by nonparametric regression: optimal rates of convergence for lower estimates

, 2010
Bermudan options, Nonparametric regression, Boundary condition, Suboptimal stopping rules, 62G08, 65C05, 60G40, G10, G12, G13,
Denis Belomestny, Belomestny, Denis
core   +1 more source

Nonparametric expectile shortfall regression for functional data

Demonstratio Mathematica
This work addresses the issue of financial risk analysis by introducing a novel expected shortfall (ES) regression model, which employs expectile regression to define the shortfall threshold in financial risk management.
Almanjahie Ibrahim M., Abood Hanan, Bouzebda Salim, Alshahrani Fatimah, Laksaci Ali   +4 more
doaj   +1 more source

Parameter Estimation of the Partially Linear Quantile Regression Model Under Monotonic Constraints

Journal of Mathematics, Volume 2025, Issue 1, 2025.
The paper brings forward the partially linear quantile regression model by incorporating monotonic constraints, which are common in real‐world relationships between variables. It introduces two novel parameter estimation methods, that is, the coordinate descent method and the profile likelihood method, which eliminate the extensive tuning and simplify ...
Shujin Wu, Zhilin Yu, Shanshan Liang, Yanke Ren, Pramita Mishra   +4 more
wiley   +1 more source

Projection Estimates of Constrained Functional Parameters [PDF]

, 2005
AMS classifications: 62G05; 62G07; 62G08; 62G20 ...
Segers, J., Guillou, A., Fils-Villetard, A.   +2 more
core  

Asymptotic optimality of a multivariate version of the generalized cross validation in adaptive smoothing splines

, 2014
We consider an adaptive smoothing spline with a piecewiseconstant penalty function λ(x), in which a univariate smoothing parameter λ in the classic smoothing spline is converted into an adaptive multivariate parameter λ.
Heeyoung Kim, X. Huo
semanticscholar   +1 more source

On internally corrected and symmetrized kernel estimators for nonparametric regression

, 2010
Multivariate regression, Smoothing matrix, Symmetry, 62G08, 62G20,
Jacho-Chávez, David, Linton, Oliver, David Jacho-Chávez, Oliver Linton   +3 more
core   +1 more source

On kernel-based estimation of conditional Kendall’s tau: finite-distance bounds and asymptotic behavior

Dependence Modeling, 2019
We study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities.
Derumigny Alexis, Fermanian Jean-David
doaj   +1 more source