This is the Brownian covariance kernel, so its integral operator is a covariance operator. To obtain its eigendecomposition, suppose with . Splitting the integral at gives
Differentiation yields and , with boundary conditions and . Hence the normalized eigenpairs are
The eigenvalues are positive and summable, consistently with positivity and the trace-class operator property of a covariance operator.
The identity operator is positive and self-adjoint, but on the infinite-dimensional Hilbert space it has eigenvalue one with infinite multiplicity. Consequently
A square-integrable Hilbert-space random variable has a trace-class covariance operator with trace . The identity therefore cannot be such a covariance operator.
This symmetric finite-rank integral operator is not positive. For , set . Directly,
The associated quadratic form is
Every covariance operator is a positive operator, so this operator is not a covariance operator.
Write for the rank-one operator . The estimator is the kernel representation of the empirical covariance operator
Since ,
Thus the estimator has the fixed bias of an estimator for .
The fourth-moment assumption makes square-integrable in the Hilbert space of Hilbert-Schmidt operators. The weak law of large numbers therefore gives
Consequently consistency for holds exactly when . More precisely, if , then
The Hilbert-space central limit theorem also yields
where is a centered Gaussian random element in the Hilbert-Schmidt operator space with covariance determined by . Relative to , the same fluctuation is displaced by and hence does not have a finite centered limit when .

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