We analyzed the effect of time and frequency domain windowing, or weighting, of the transmitted signal for least squares (LS) and minimum mean-square error (MMSE) estimators when the channel is time-variant. We considered the estimation error for different windows and we found that the windows can be selected independently. We explained the performance of the estimators with the help of the rad...

The SeaWinds on QuikScat scatterometer is the first in a series of new scanning pencil-beam Ku-band scatterometers. The viewing geometry is significantly different than previous fan beam instruments, resulting in different characteristics in the retrieved winds. In this paper we provide an assessment of the reliability of the SeaWinds ambiguity selection using a SeaWinds data-only algorithm. An...

Abstract We discuss stochastic optimization problems under distributional ambiguity. The uncertainty is captured by considering an entire family of distributions. Because we assume the existence data, can consider confidence regions for different estimators parameters Based on definition appropriate estimator in interior resulting region, propose a new data-driven problem. This approach applies...

We develop a tractable method to estimate multiple prior models of decision-making under ambiguity. In a representative sample of the U.S. population, we measure ambiguity attitudes in the gain and loss domains. We find that ambiguity aversion is common for uncertain events of moderate to high likelihood involving gains, but ambiguity seeking prevails for low likelihoods and for losses. We show...

We propose a Bayesian framework for assessing the relative strengths of data-driven ambiguity sets in distributionally robust optimization (DRO) when the underlying distribution is defined by a finite-dimensional parameter. The key idea is to measure the relative size between a candidate ambiguity set and a specific asymptotically optimal set. As the amount of data grows large, this asymptotica...