Parameter estimation by contrast minimization for noisy observations of a diffusion process

We consider the estimation of unknown parameters in the drift and diffusion coefficients of a one-dimensional ergodic diffusion X when the observation Y is a discrete sampling of X with an additive noise, at times i δ, i=1,?…?, N. Assuming that the sampling interval tends to 0 while the total length-time interval tends to infinity, we prove limit theorems for functionals associated with the observations, based on local means of the sample. We apply these results to obtain a contrast function. The associated minimum contrast estimators are shown to be consistent. Some examples are discussed with numerical simulations.     

Options Contracts with Overbooking in the Air Cargo Industry

In today's world economy, which is marked by intensified international trade, air cargo acts as a key facilitator. However, cargo airlines continue to struggle to be profitable because of very high asset costs and substantial demand uncertainty. To improve upon this situation, we propose an options contract. Our model captures the main features of cargo trade between an airline and a freight forwarder and allows us to derive an optimal reservation policy. We then go on to analyze the impact of overbooking on the profit of the cargo capacity provider. The model is subsequently applied to real‐life booking data provided by a major cargo carrier. This enables us to compare current contractual arrangements with the ones proven optimal in the model. A numerical study provides insights about the impact of overbooking on contract parameters and profitability. Managerial insights to be drawn conclude this study.     

Supply Chains in Sub‐Saharan Africa: A Decision Support System for Small‐Scale Seed Entrepreneurs*

It is necessary to infuse a consistent supply of improved seed varieties into local sub‐Saharan African crop production to improve low crop yields. The best distribution channel for the improved seed varieties may be small‐scale commercial seed companies, but local entrepreneurs struggle to determine whether such businesses are viable. Using a multi‐echelon supply chain approach, a decision support system (DSS) was designed to help African seed entrepreneurs make informed decisions about small‐scale seed chain businesses. Specifically, entrepreneurs make decisions about where to locate seed enterprises, with which farmers to contract, and where to store seed. Optimization and simulation modeling are used to evaluate infrastructure variables such as distance, transportation cost, and storage loss and cost in three development level areas. Currently, the decision tool is used in Mozambique, Malawi, Kenya, and Tanzania. The model has supported the start‐up of at least 17 small seed companies that are now introducing improved seed varieties into villages and farms. The DSS applies decision science research in a humanitarian application and offers important managerial implications about supply chain infrastructure to nongovernmental organizations and humanitarian groups. Such applications are vital as groups such as USAID, the Gates Foundation, and the International Crops Research Institute for the Semi‐Arid Tropics (ICRISAT) continue to move toward micro‐enterprise, value chain, and market‐oriented development programs.     

Hierarchical Bayesian modeling of marked non-homogeneous Poisson processes with finite mixtures and inclusion of covariate information

We investigate marked non-homogeneous Poisson processes using finite mixtures of bivariate normal components to model the spatial intensity function. We employ a Bayesian hierarchical framework for estimation of the parameters in the model, and propose an approach for including covariate information in this context. The methodology is exemplified through an application involving modeling of and inference for tornado occurrences.     

Penalized and Shrinkage Estimation in the Cox Proportional Hazards Model

This article considers the shrinkage estimation procedure in the Cox's proportional hazards regression model when it is suspected that some of the parameters may be restricted to a subspace. We have developed the statistical properties of the shrinkage estimators including asymptotic distributional biases and risks. The shrinkage estimators have much higher relative efficiency than the classical estimator, furthermore, we consider two penalty estimators—the LASSO and adaptive LASSO—and compare their relative performance with that of the shrinkage estimators numerically. A Monte Carlo simulation experiment is conducted for different combinations of irrelevant predictors and the performance of each estimator is evaluated in terms of simulated mean squared error. Simulation study shows that the shrinkage estimators are comparable to the penalty estimators when the number of irrelevant predictors in the model is relatively large. The shrinkage and penalty methods are applied to two real data sets to illustrate the usefulness of the procedures in practice.     

Markov Systems in a General State Space

In this article, we introduce and study Markov systems on general spaces (MSGS) as a first step of an entire theory on the subject. Also, all the concepts and basic results needed for this scope are given and analyzed. This could be thought of as an extension of the theory of a non homogeneous Markov system (NHMS) and that of a non homogeneous semi-Markov system on countable spaces, which has realized an interesting growth in the last thirty years. In addition, we study the asymptotic behaviour or ergodicity of Markov systems on general state spaces. The problem of asymptotic behaviour of Markov chains has been central for finite or countable spaces since the foundation of the subject. It has also been basic in the theory of NHMS and NHSMS. Two basic theorems are provided in answering the important problem of the asymptotic distribution of the population of the memberships of a Markov system that lives in the general space (X, ?(X)). Finally, we study the total variability from the invariant measure of the Markov system given that there exists an asymptotic behaviour. We prove a theorem which states that the total variation is finite. This problem is known also as the coupling problem.     

Optimal Quantity Discount Design with Limited Information Sharing*

In this article we address the optimal quantity discount design problem of a supplier in a two‐stage supply chain where the supplier and the buyer share annual demand information only. The supply chain faces a constant deterministic demand that is not price sensitive and operates with fixed setup costs in both stages. We show that the supplier can actually moderate a cost‐minimizing buyer to order in quantities different than the buyer's optimal order quantity in the traditional setting and develop a multi‐breakpoint quantity discount scheme that maximizes supplier's expected net savings. The proposed multi‐breakpoint discount scheme can be easily computed from the available information and, while also maximizing the supplier's net savings, is very effective in achieving high levels of supply chain coordination efficiency in the presence of limited information.