The mean-variance model for portfolio selection requires estimates of many parameters. This paper investigates the effect of errors in parameter estimates on the results of mean-variance analysis. Using a small amount of historical data to estimate parameters exposes the model to estimation errors. However, using a long time horizon to estimate parameters increases the possibility of nonstation...

Spectral analysis of the vibration signals of industrial steam turbines provides efficient reference for the characterization and discrimination of turbine faults. Conventional power spectrum estimation methods often exhibit contradiction between variance performance and resolution, leading to poor estimation results. In this study, we investigated Levision-Durbin recursive algorithm, Burg algo...

This paper discusses the estimation of generalization gap, difference between performance and training performance, for overparameterized models including neural networks. We first show that a functional variance, key concept in defining widely-applicable information criterion, characterizes gap even settings where conventional theory cannot be applied. As computational cost variance is expensi...