Inference about a scalar parameter of interest typically relies on the asymptotic normality common likelihood pivots, such as signed root, score and Wald statistics. Nevertheless, resulting inferential procedures have been known to perform poorly when dimension nuisance is large relative sample size information parameters limited. In cases, use analytical modifications root recover performance....

Monitoring machine learning models once they are deployed is challenging. It even more challenging to decide when retrain in real-case scenarios labeled data beyond reach, and monitoring performance metrics becomes unfeasible. In this work, we use non-parametric bootstrapped uncertainty estimates SHAP values provide explainable estimation as a technique that aims monitor the deterioration of de...

The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to usc Monte Carlo simulation, based on a non-parametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric model describing the demand for capital, labor, energy, and materials. The model is fitted by three-stage least squares. In sharp contrast with previou...

This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable serially correlated random errors. The random errors are modeled by an autoregressive time series. We show that the distributions of the feasible semiparametric generalized least squares estimator o...