Clustering addresses the problem of finding homogeneous and well-separated subsets, called clusters, from a set of given data points. In addition to the poi...

The \(k\)-means is a benchmark algorithm used in cluster analysis. It belongs to the large category of heuristics based on location-allocation steps that ...

The balanced clustering problem consists of partitioning a set of \(n\) objects into \(K\) equal-sized clusters as long as \(n\) is a multiple of `(K...

This paper examines the issue of price and advertising coordination in bilateral monopolies from a dynamic perspective. Its main objectives are to design i...

We consider within the framework of Mean Field Games theory a dynamic discrete choice model with an advertiser, where a large number of minor agents (e.g., c...

In this paper we propose a new variant of the Variable Neighborhood Decomposition Search (VNDS) heuristic for solving global optimization problems and apply ...

Extreme Learning Machine (ELM) has recently increased popularity and has been successfully applied to a wide range of applications. Variants using regulariza...

The technological developments observed in the last two decades contributed to the digitalization of products and the introduction of devices to consume th...

We introduce an iterative algorithm for the solution of the diameter minimization clustering problem (DMCP). Our algorithm is based upon two observations: 1)...

In this paper, we propose a diffusion model for a subscription service. The evolution over time of the number of subscribers is governed by a differential ...

This longitudinal quantitative study investigates how organizational structure and the external environment impact VC firm survival. It examines how macroeco...

This paper investigates the dynamic pricing strategies of firms selling complementary products in a marketing channel. The problem is modelled as a non-coo...

We propose a method to build trees and forests when the response is a non-homogeneous Poisson process with excess zeros, based on two forests. The first one...

Clustering is a data mining method which consists in partitioning a given set of n objects into p clusters in order to minimize the dissimilarity among o...

We investigate the impact of store capacity and extent of inter-product substitution in a retailer's assortment on the optimal timing and depth of price pro...

In this paper, we extend the classical market-share attraction model to a multi-category setting to include umbrella-branding spillover. Our starting conject...

We revisit the relationship between market power and firms' investment incentives in a noncooperative differential oligopoly game in which firms sell diffe...

We characterize equilibrium pricing strategies in a marketing channel in two scenarios. In the first scenario, the manufacturer chooses the wholesale price...

We consider the multivariate linear model for multilevel data where units are nested within a hierarchy of clusters. We propose permutation procedures to tes...

In this paper, we propose a multivariate random forest method for multiple responses of mixed types with missing responses. Imputation is performed for eac...