Imputation of missing data is important in many areas, such as reducing non-response bias in surveys and maintaining medical documentation. Estimating the uncertainty inherent in the imputed values is one way of evaluating the results of the imputation process. This paper presents a new method for the estimation of imputation uncertainty, which can be implemented as part of any imputation metho...

Computational chemistry is a largely empirical field that makes predictions with substantial uncertainty. And yet the use of standard statistical methods to quantify this uncertainty is often absent from published reports. This article covers the basics of confidence interval estimation for molecular modeling using classical statistics. Alternate approaches such as non-parametric statistics and...

A control analysis and design framework is proposed for systems subject to parametric uncertainty. The underlying strategies are based on sum-of-squares (SOS) polynomial analysis and nonlinear optimization to design an optimally robust controller. The approach determines a maximum uncertainty range for which the closed-loop system satisfies a set of stability and performance requirements. These...

In this paper we show that measures of economic uncertainty (conditional volatility of consumption) predict and are predicted by valuation ratios at long horizons. Further we document that asset valuations drop as economic uncertainty rises — that is, financial markets dislike economic uncertainty. Moreover, future earnings growth rates are sharply predicted by current price-earnings ratios. It...

BACKGROUND Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to re...

Traditionally the forecast evaluation literature has focused on methods for assessing point forecasts and interval forecasts. Recently , however, density forecasting became very active and important area of research in the analysis of economic and financial time series with the introduction of simple and operational framework for density forecast evaluation by Diebold et al (1998). Density fore...

Quantitative methods for characterizing variability and uncertainty were applied to case studies of oxides of nitrogen and total organic carbon emission factors for lean-burn natural gas-fueled internal combustion engines. Parametric probability distributions were fit to represent inter-engine variability in specific emission factors. Bootstrap simulation was used to quantify uncertainty in the...

This paper develops a methodology for uncertainty quantification and model validation in the presence of epistemic uncertainty due to sparse and imprecise data. Three types of epistemic uncertainty regarding input random variables – interval data, sparse point data, and probability distributions with parameter uncertainty – are considered. When the model inputs are described using sparse point ...

In this research, first, kinematic and dynamic equations of a 4-DOF 3-link robotic finger are derived using Denavit-Hartenberg convention and Lagrange’s formulation. To model the muscles, several springs and dampers are placed between the finger links. Then, two advanced controllers, namely adaptive-robust and adaptive-neural, which can control the robotic finger in presence of parametric uncer...