We provide novel, high-order accurate methods for non-parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences ...

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...

This is a preview. Log in through your library . Abstract We show that difference-based methods can be used to construct simple and explicit estimators of error ...

Non-parametric tests are used when standard assumptions are not available. These tests don’t rely on distributions, often ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...