Let $\{X\_i, i \in \mathbb{N}\}$ be a sequence of non-lattice i.i.d. centered random variables, $\mathbb{E} |X\_1| ^3 < 0$. Let $S\_n = \sum\limits \_{i=1} ^n X\_i$ be the corresponding random walk and $W^{(n)} \_t = \frac{S\_{\lfloor nt \rfloor}}{\sqrt{n}}$, $t \in [0,1]$.
I am looking for a reference that conditioned on the end point $S\_n$, the normalized trajectory $W^{(n)}$ converges to a Brownian bridge: for a sequence $a\_n$ such that $\frac{a\_n}{\sqrt{n}} \to a \in \mathbb{R}$,


$$
W^{(n)} \big| S\_n \in [a\_n, a\_n+1] \Rightarrow B^a,
$$
where $(B\_t, t\in [0,1])$ is a Brownian bridge and $B^a\_t = B\_t + at$.
In other words, for every continuous functional $f : C[0,1] \to \mathbb{R}$,


$$
\mathbb{E} \Big \{ f (W^{(n)}) \Big| S\_n \in [a\_n, a\_n+1] \Big\} \Rightarrow f(B^a).
$$



Invariance principle: Brownian bridge and random walk conditioned on end point
A more general theorem is proved in [1] for the limits of random walks in the domain of attraction of a stable law. In the case described in the problem, one can also use the strong approximation approach in [2], because the probabilities of deviations of order $\sqrt{n}$ are smaller than the probabilities of the events that are being conditioned on.


[1] Liggett, Thomas M. "An invariance principle for conditioned sums of independent random variables." Journal of Mathematics and Mechanics 18, no. 6 (1968): 559-570.
<https://www.jstor.org/stable/pdf/24901780.pdf>


[2] Csörgo, Miklos, and Pál Révész. Strong approximations in probability and statistics. Academic press, 2014.


