This paper studies measurement errors that subtract signal from true variables of interest, labeled lack of signal errors (LoSE). The effect on OLS regression of LoSE is opposite the conventional wisdom about classical measurement errors, with LoSE in the dependent variable, not the explanatory...

This paper investigates the effects of using residuals from robust regression in place of OLS residuals in test statistics for the normality of the errors. It is found that for systematic and clustered outliers robustified normality tests yield greater power

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately,...

This note considers a nonlinear regression model containing a 0-1 dichotomous regressor when it is subject to arbitrary measurement errors in the sample. The parameter of interest is the effect of the latent dichotomous variable on the dependent variable. Given that the measurement errors are...

Investors sometimes have strong convictions that a distinctive economic regime will prevail in the period ahead and therefore would like to form a portfolio that reflects the expected returns, standard deviations, and correlations of assets during such a regime. To do so, they typically isolate...

Sharpe style regression has become a widespread analytic tool in the financial community. The style regression allows one to investigate such interesting issues as style composition, style sensitivity, and style change over time. All previous methods to obtain the distribution and confidence...