2016/11/22 by Nathalie Akakpo, Akakpo, Nathalie
Computer Science · #Advanced Image Processing Techniques #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Mathematics #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Statistics Theory (math.ST)
paper · doi:10.48550/arxiv.1611.07237
openalex publication_date 2016/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new statistical procedure able in some way to overcome the curse of dimensionality without structural assumptions on the function to estimate. It relies on a least-squares type penalized criterion and a new collection of models built from hyperbolic biorthogonal wavelet bases. We study its properties in a unifying intensity estimation framework, where an oracle-type inequality and adaptation to mixed smoothness are shown to hold. Besides, we describe an algorithm for implementing the estimator with a quite reasonable complexity.