In this work, we consider a modification of the usual branching random walk (BRW), where we give certain independent and identically distributed (i.i.d.) displacements to all the particles at the n-th generation, which may be different from the driving increment distribution. We call this process last progeny modified branching random walk (LPM-BRW). Depending on the value of a parameter, \(\theta \) , we classify the model into three distinct cases, namely, the boundary case, below the boundary case, and above the boundary case. Under very minimal assumptions on the underlying point process of the increments, we show that at the boundary case, \(\theta =\theta _0\) , where \(\theta _0\) is a parameter value associated with the displacement point process, the maximum displacement converges to a limit after only an appropriate centering, which is of the form \(c_1 n - c_2 \log n\) . We give an explicit formula for the constants \(c_1\) and \(c_2\) and show that \(c_1\) is exactly the same, while \(c_2\) is 1/3 of the corresponding constants of the usual BRW [2]. We also characterize the limiting distribution. We further show that below the boundary, \(\theta < \theta _0\) , the logarithmic correction term is absent. For above the boundary, \(\theta > \theta _0\) , the logarithmic correction term is exactly the same as that of the classical BRW. For \(\theta \le \theta _0\) , we further derive Brunet–Derrida-type results of point process convergence of our LPM-BRW to a Poisson point process. Our proofs are based on a novel method of coupling the maximum displacement with a linear statistic associated with a more well-studied process in statistics, known as the smoothing transformation.
