Results 31 to 40 of about 242 (53)
Identifying spatial point sets [PDF]
, 2003 Two sets of spatial points are checked whether they can pproximately be transformed into each other by applying some unknown translation and some unknown rotation. This problem occurs at least in two dimensions within computational metrology.
H. Späth
core
The notion of breakdown point was introduced by Hampel (1968, 1971) and has since played an important role in the theory and practice of robust statistics.
Davies, P. Laurie, Gather, Ursula
core
Least squares fitting of conic sections with type identification by NURBS of degree two [PDF]
, 1999 Fitting of conic sections is used in reflectometry, aircraft industry, metrology, computer vision, astronomy and propagation of sound waves [5]. So far numerical algorithms assume the type of the conic section to be known in advance.
H. Späth, I. Seufer
core
Sparse approximation of multilinear problems with applications to kernel-based methods in UQ
, 2017 We provide a framework for the sparse approximation of multilinear problems and show that several problems in uncertainty quantification fit within this framework.
Nobile, Fabio +2 more
core +1 more source
, 2004 The notion of breakdown point was introduced by Hampel (1968, 1971) and has since played an important role in the theory and practice of robust statistics.
Davies, P. L., Gather, U.
core +1 more source
Recursive kernel density estimators under missing data
, 2016 In this paper we propose an automatic bandwidth selection of the recursive kernel density estimators with missing data in the context of global and local density estimation.
Slaoui, Yousri
core +3 more sources
Two new nonparametric kernel distribution estimators based on a transformation of the data. [PDF]
J Appl Stat, 2021 Slaoui Y.
europepmc +1 more source
Smoothed corners and scattered waves
, 2016 We introduce an arbitrary order, computationally efficient method to smooth corners on curves in the plane, as well as edges and vertices on surfaces in $\mathbb R^3$.
Epstein, Charles L., O'Neil, Michael
core +1 more source
R 0 estimation for COVID-19 pandemic through exponential fit. [PDF]
Math Methods Appl Sci, 2022 Mingliang Z, Simos TE, Tsitouras C.
europepmc +1 more source
Convex-Concave fitting to successively updated data and its application to covid-19 analysis. [PDF]
J Comb Optim, 2022 Davos DE, Demetriou IC.
europepmc +1 more source