Advances in Applied Probability
An asymptotic approach to centrally planned portfolio selection
We formulate a centrally planned portfolio selection problem with the investor and the manager having S-shaped utilities under a recently popular first-loss contract. We solve for the closed-form optimal portfolio, which shows that a first-loss contrac…
Advances in Applied Probability
Central limit theorem for a birth–growth model with poisson arrivals and random growth speed
We consider Gaussian approximation in a variant of the classical Johnson–Mehl birth–growth model with random growth speed. Seeds appear randomly in at random times and start growing instantaneously in all directions with a random speed. The locations,…
Advances in Applied Probability
The location of high-degree vertices in weighted recursive graphs with bounded random weights
We study the asymptotic growth rate of the labels of high-degree vertices in weighted recursive graphs (WRGs) when the weights are independent, identically distributed, almost surely bounded random variables, and as a result confirm a conjecture by Lod…
Advances in Applied Probability
Local weak limit of preferential attachment random trees with additive fitness
We consider linear preferential attachment trees with additive fitness, where fitness is the random initial vertex attractiveness. We show that when the fitnesses are independent and identically distributed and have positive bounded support, the local …
Advances in Applied Probability
Concentration of measure for graphon particle system
We study heterogeneously interacting diffusive particle systems with mean-field-type interaction characterized by an underlying graphon and their finite particle approximations. Under suitable conditions, we obtain exponential concentration estimates o…
Advances in Applied Probability
Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments
Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in terms of their parents according to a directed acyclic graph (DAG). We …