Solution
ID: past-exam-of-the-mathematics-course-of-the-university-of-cambridge/2024/iii/paper-210/2/solution
Past exam of the mathematics course of the University of Cambridge 2024 iii Paper 210 2 Solution by
Codex 0 Created 2026-09-24 Updated 2026-09-25
A kernel for density estimation is an integrable function with . With , the kernel density estimator isA kernel of order ell satisfies
Split the integrated risk into stochastic and bias terms:For fixed , put and . The supplied Rosenthal inequality givesNowAlso . The Young convolution inequality with exponent givesAfter integration and multiplication by the outer factor , the stochastic contribution is at most
It remains to control the bias. Here . The Taylor formula with integral remainder, the vanishing kernel moments, and Minkowski integral inequality giveThe defining Nikol'skii smoothness bound is , soRaising to the th power and applying the outer factor proves
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