A Neyman-orthogonal estimator for risk heterogeneity between groups is consistent and asymptotically normal, reduces finite-sample bias relative to likelihood methods in simulations, and identifies ethnicity-specific effects in eICU mortality data that standard approaches miss.
Common functional principal components
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abstract
Functional principal component analysis (FPCA) based on the Karhunen--Lo\`{e}ve decomposition has been successfully applied in many applications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only by the theoretical challenge of this data situation, but also by the actual question of dynamics of implied volatility (IV) functions. For different maturities the log-returns of IVs are samples of (smooth) random functions and the methods proposed here study the similarities of their stochastic behavior. First we present a new method for estimation of functional principal components from discrete noisy data. Next we present the two sample inference for FPCA and develop the two sample theory. We propose bootstrap tests for testing the equality of eigenvalues, eigenfunctions, and mean functions of two functional samples, illustrate the test-properties by simulation study and apply the method to the IV analysis.
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stat.ME 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Robust inference for risk heterogeneity under group imbalance
A Neyman-orthogonal estimator for risk heterogeneity between groups is consistent and asymptotically normal, reduces finite-sample bias relative to likelihood methods in simulations, and identifies ethnicity-specific effects in eICU mortality data that standard approaches miss.