This paper analyzes the behavior of a central bank under strong (Knightian) uncertainty when the short run trade-off between output and inflation is represented by the Sticky Information Phillips Curve recently proposed by Mankiw and Reis (2002). By solving the robust control problem...

Optimization of simulated systems is tackled by many methods, but most methods assume known environments. This article, however, develops a 'robust' methodology for uncertain environments. This methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by...

This paper has elaborated upon the deleterious effects of outliers and corruption of dataset on estimation of linear regression coefficients by the Ordinary Least Squares method. Motivated to ameliorate the estimation procedure, we have introduced the robust regression estimators based on...

We develop infinitesimally robust statistical procedures for general diffusion processes. We first prove existence and uniqueness of the times series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale)...

The goal of robust parameter design experiments is to identify significant location and dispersion factors that can be used to set the mean response at the target level and to decrease the sensitivity of the response to uncontrolled noise factors. We present a hierarchical Bayesian model and use...

This paper concerns dynamic pricing of multiple perishable products when there is model uncertainty, which we formulate as a worst-case stochastic intensity control problem where ambiguity is modeled using the notion of relative entropy. One feature of our formulation is that the demand models...

In this note we consider several versions of the bootstrap and argue that it can be helpful in explaining and thinking about such procedures to use an explicit representation of the random resampling process. To illustrate the point we give such explicit representations and use them to produce...

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gaussian reference model. Based on these...

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...

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...