On ɠ-statistical approximation of wavelets aided Kantorovich ɠ-Baskakov operators
M. Mursaleen, P. Lamichhane, Adem Kılıçman, Norazak Senu
Abstract
The aim of this research is to examine various statistical approximation properties of Kantorovich ?-Baskakov operators using wavelets. We discuss and investigate the weighted statistical approximation employing a Bohman-Korovkin type theorem as well as a statistical rate of convergence applying a weighted modulus of smoothness ??? correlated with the space B?? (R+) and Lipschitz type maximal functions.
Topics & Concepts
MathematicsWaveletBaskakov operatorApplied mathematicsCalculus (dental)Mathematical analysisOperator theoryOrthodonticsMicrolocal analysisArtificial intelligenceFourier integral operatorComputer scienceMedicineApproximation Theory and Sequence SpacesMathematical Analysis and Transform MethodsImage and Signal Denoising Methods