 Solution

PutWith the $n$ by $K$ design matrix $\Xi =({\xi }_{ik})$, the scalar-on-function linear model becomesThe ordinary least squares estimator based on the truncated model isThus the omitted tail produces the conditional bias $({\Xi }^{\mathsf{T}}\Xi {)}^{-1}{\Xi }^{\mathsf{T}}r$.

LetThe supplied weak law of large numbers and noise limit giveFor a general fixed basis, $g$ need not vanish, so the retained coefficients are asymptotically biased and the estimator is inconsistent even for $c$. Moreover, with fixed $K$ it cannot recover the full slope $\beta$ when $\beta -{\beta }_K\ne 0$.