Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Charles C Taylor"'
Publikováno v:
Statistical Methods & Applications.
We study the problem of estimating a regression function when the predictor and/or the response are circular random variables in the presence of measurement errors. We propose estimators whose weight functions are deconvolution kernels defined accord
Publikováno v:
Journal of Statistical Computation and Simulation. 91:2289-2306
The package implements nonparametric (smooth) regression for spherical data in , and is freely available from the Comprehensive Archive Network (CRAN), licensed under the MIT License. It can be use...
Publikováno v:
J Appl Stat
We develop a new method that combines a decision tree with a wavelet transform to forecast time series data with spatial spillover effects. The method can not only improve prediction but also give good interpretability of the time series mechanism. A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d3e48ec1c09779558332958a62f626a
https://europepmc.org/articles/PMC10291940/
https://europepmc.org/articles/PMC10291940/
Publikováno v:
Journal of Statistical Computation and Simulation. 90:1965-1981
Two-sample tests are probably the most commonly used tests in statistics. These tests generally address one aspect of the samples' distribution, such as mean or variance. When the null hypothesis is that two distributions are equal, the Anderson–Da
Until now the problem of estimating circular densities when data are observed with errors has been mainly treated by Fourier series methods. We propose kernel-based estimators exhibiting simple construction and easy implementation. Specifically, we c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::310c823f796d299e7297f2e7a0b3ebfc
Publikováno v:
Advances in Data Analysis and Classification. 15:5-36
In forecasting, we often require interval forecasts instead of just a specific point forecast. To track streaming data effectively, this interval forecast should reliably cover the observed data and yet be as narrow as possible. To achieve this, we p
Publikováno v:
Biotechnol Bioeng
Breast cancer cells experience a range of shear stresses in the tumor microenvironment (TME). However most current in vitro three-dimensional (3D) models fail to systematically probe the effects of this biophysical stimuli on cancer cell metastasis,
Publikováno v:
Springer Proceedings in Mathematics & Statistics ISBN: 9783030573058
We consider the problem of nonparametrically estimating a circular density from data contaminated by angular measurement errors. Specifically, we obtain a kernel-type estimator with weight functions that are reminiscent of deconvolution kernels. Here
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::154b31f6ca0604865d5eab56b284ff24
https://doi.org/10.1007/978-3-030-57306-5_17
https://doi.org/10.1007/978-3-030-57306-5_17
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
LOD
The problem of computing distances and shortest paths between vertices in graphs is one of the fundamental issues in graph theory. It is of great importance in many different applications, for example, transportation, and social network analysis. How
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17bd8aac692e121f9efce0fa5bee2dd3
https://doi.org/10.1007/978-3-030-13709-0_17
https://doi.org/10.1007/978-3-030-13709-0_17
Classifying observations coming from two different spherical populations by using a nonparametric method appears to be an unexplored field, although clearly worth to pursue. We propose some decision rules based on spherical kernel density estimation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d97cf444bc4f7639d6f258f7d69179a
https://eprints.whiterose.ac.uk/138925/1/sphereclassA5.pdf
https://eprints.whiterose.ac.uk/138925/1/sphereclassA5.pdf