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• W2788891689 abstract "A common challenge in nonparametric inference is its high computational complexity when data volume is large. In this paper, we develop computationally efficient nonparametric testing by employing a random projection strategy. In the specific kernel ridge regression setup, a simple distance-based test statistic is proposed. Notably, we derive the minimum number of random projections that is sufficient for achieving testing optimality in terms of the minimax rate. An adaptive testing procedure is further established without prior knowledge of regularity. One technical contribution is to establish upper bounds for a range of tail sums of empirical kernel eigenvalues. Simulations and real data analysis are conducted to support our theory." @default.
• W2788891689 created "2018-03-06" @default.
• W2788891689 creator A5042490620 @default.
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• W2788891689 date "2018-02-17" @default.
• W2788891689 modified "2023-09-27" @default.
• W2788891689 title "Nonparametric Testing under Random Projection" @default.
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• W2788891689 hasPublicationYear "2018" @default.
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