We discuss the use of robust analysis of variance (ANOVA) techniques as applied to quality engineering. ANOVA is the cornerstone for uncovering the effects of design factors on performance. Our goal is to utilize methodologies that yield similar results to standard methods when the underlying...

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...

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...

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...