A new stepwise regression program (SROV) for the construction of optimal (stable and of highest possible accuracy) regression models comprised of linear combination of independent variables and their non-linear functions is described. The program uses for regression QR decomposition based on Gram /Schmidth orthogonalization, which is highly resilient to numerical error propagation. Variables ar...

We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to for...

this paper empirically investigates the relationship between cpi inflation uncertainty, and private investment in the iranian economy from 1988 to 2010 by using quarterly data. we employ a bivariate var(5)-garch(1,1)-in-mean with diagonal bekk model to discover in a unified framework how are the interactions between the variables. in the model, conditional variance of inflation and private inve...

We suggest a method for simultaneous variable selection and outlier identification based on the computation of posterior model probabilities. This avoids the problem that the model you select depends upon the order in which variable selection and outlier identification are carried out. Our method can find multiple outliers and appears to be successful in identifying masked outliers. We also add...

This paper constructs a model economy to explain how uncertainty shocks can cause business cycle fluctuations. In the model, during times of high uncertainty, the representative agent, due to risk aversion motives, moves resources from a high risk/high return production process to a low risk/low return production process. High uncertainty thus causes the firm level technology frontier to endoge...

Analysts are increasingly being required to evaluate the uncertainty associated with their methods. Indeed, estimating the uncertainty of an analytical result is an essential part of quantitative analysis. The approach of the International Organisation for Standardisation to uncertainty estimation requires the identification of the possible sources of uncertainty for a procedure, followed by th...

A theoretical model for uncertainty sources identification in tip-timing measurement systems

making capital and investment is the main driving forces of economic development. based on the investment sensitivity to the changes of some of macro-economic variables and risk and uncertainty, the present study evaluated the effective factors on investment in private sector in iran during 1980-2007. at first, the uncertainty variables of real informal exchange rate, nominal interest rate and ...

In this paper we describe an application of a robust identification approach to a turbofan engine. The approach combines classical time-data system identification via prediction error models and the so-called model error model theory. The model error model approach provides guidelines for the characterization of the model uncertainty stemming from the nominal system identification and fits natu...

This paper introduces the concepts of equipment test qualification and uncertainty quantification, analysis framework process quantification. It analyzes data uncertainty, model environmental studies corresponding quantification theory to provide technical reference for application technology in field identification.