Let be the interpolation in part (a). The operation
is continuous, since . By the Donsker invariance principle and the continuous mapping theorem,
The exact trapezoidal integral of the linear interpolation is
Therefore the statistic in question differs from by . Using independence, zero means, and unit variances gives
This error tends to zero in , hence in probability. The Slutsky theorem now proves the integrated random-walk limit:
The limiting law can also be made explicit. The time integral is a Gaussian random variable, as a mean-square limit of linear combinations of a Gaussian process. It is centered, and the covariance identity gives
Thus the terminal value of integrated Brownian motion here has law .