Results 301 to 310 of about 1,593,883 (356)

Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing

AAAI Conference on Artificial Intelligence
We address the Individualized continuous treatment effect (ICTE) estimation problem where we predict the effect of any continuous valued treatment on an individual using ob- servational data.
Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi   +3 more
semanticscholar   +1 more source

Kernel covariance series smoothing

2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over ...
Cristina Soguero-Ruiz, Robert Jenssen
openaire   +1 more source

KERNEL REGRESSION SMOOTHING OF TIME SERIES

Journal of Time Series Analysis, 1992
Abstract. A class of non‐parametric regression smoothers for times series is defined by the kernel method. The kernel approach allows flexible modelling of a time series without reference to a specific parametric class. The technique is applicable to detection of non‐linear dependences in time series and to prediction in smooth regression models with ...
Härdle, Wolfgang, Vieu, Philippe
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Smoothed Bagging with Kernel Bandwidth Selectors

Neural Processing Letters, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lee, Shinjae, Cho, Sungzoon
openaire   +2 more sources

Source Region Identification Using Kernel Smoothing

Environmental Science & Technology, 2009
As described in this paper, nonparametric wind regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of pollutants as defined by wind direction sectors. It is described in detail with an example of its application to SO2 data from East St. Louis, IL.
Ronald, Henry, Gary A, Norris, Ram, Vedantham, Jay R, Turner   +3 more
openaire   +2 more sources

Smooth Bayesian Kernel Machines

2005
In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order.
Rutger W. ter Borg, Léon J. M. Rothkrantz   +1 more
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Eigenvalues of smooth kernels

Mathematical Proceedings of the Cambridge Philosophical Society, 1984
Suppose is a symmetric square integrable kernel on the unit square [0, 1]2. Thenis a compact symmetric operator on the Hilbert space L2[0, 1]. H. Weyl (see [2]) has shown that, if then the eigenvaluesof T satisfy as n → ∞. We prove a related result.
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Smoothed kernel conditional density estimation

Economics Letters, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wen, Kuangyu, Wu, Ximing
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Kernel smoothing for finite populations

Statistics and Computing, 1993
We identify a role for smooth curve provision in the finite population context. The performance of kernel density estimates in this scenario is explored, and they are tailored to the finite population situation especially by developing a method of data-based selection of the smoothing parameter appropriate to this problem. Simulated examples are given,
M. C. Jones, I. S. Bradbury
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Smooth Optimum Kernel Estimators Near Endpoints

Biometrika, 1991
SUMMARY Kernel estimators for smooth curves like density, spectral density or regression functions require modifications when estimating near endpoints of the support, both for practical and asymptotic reasons. The construction of such boundary kernels as solutions of a variational problem is addressed and representations in orthogonal polynomials are ...
openaire   +1 more source