We introduce a class of new sharing arrangements in a multi-stage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast...

We study a two-stage supply chain where the retailer observes two demand streams coming from two consumer populations. We further assume that each demand sequence is a station- ary Autoregressive Moving Average (ARMA) process with respect to a Gaussian white noise sequence (shocks). The shock...

We introduce a class of new sharing arrangements in a multi-stage supplychain in which the retailer observes stationary autoregressive movingaverage demand with Gaussian white noise (shocks). Similar to previousresearch, we assume each supply chain player constructs its best linearforecast of...

We consider the problem of assessing value of demand sharing in a multi-stage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best...

We consider a two-tier inventory management system with one retailer and one supplier. The retailer serves a demand driven by a stationary moving average process (of possibly infinite order) and places periodic inventory replenishment orders to the supplier. In this setting, we study the value...

In this paper, we revisit the problem of demand propagation in a multi-stage supply chainin which the retailer observes ARMA demand. In contrast to previous work, we show how eachplayer constructs the order based upon its best linear forecast of leadtime demand given itsavailable information. In...

Nonparametric regression techniques provide an e ective way of identifying and examiningstructure in regression data The standard approaches to nonparametric regression suchas local polynomial and smoothing spline estimators are sensitive to unusual observations and alternatives designed to be...

Nonparametric regression techniques provide an effective way of identifying and examining structure in regression data. The standard approaches to nonparametric regression, such as local polynomial and smoothing splineestimators, are sensitive to unusual observations, and alternatives designedto...

The least squares linear regression estimator is well-known to be highly sensitive tounusual observations in the data, and as a result many more robust estimators havebeen proposed as alternatives. One of the earliest proposals was least-sum of absolutedeviations (LAD) regression, where the...

This paper discusses a novel application of mathematical programming techniques to a regression problem. While least squares regression techniques have been used fora long time, it is known that their robustness properties are not desirable. Specifically, the estimators are known to be too...