This short course will provide an overview of non-parametric statistical techniques. The course will first describe what non-parametric statistics are, when they should be used, and their advantages ...
We use influence functions as a basic tool to study unconditional nonparametric and parametric expected shortfall (ES) estimators with regard to returns data influence, standard errors and coherence.
International Monetary Fund: Comparing Parametric and Non-parametric Early Warning Systems for Currency Crises in Emerging Market Economies
The purpose of this paper is to compare in-sample and out-of-sample performances of three parametric and non-parametric early warning systems (EWS) for currency crises in emerging market economies ...
Comparing Parametric and Non-parametric Early Warning Systems for Currency Crises in Emerging Market Economies
Parametric tests make assumptions that aspects of the data follow some sort of theoretical probability distribution. Non-parametric tests or distribution free methods do not, and are used when the ...
Explain why and when a non-parametric test should be used or may be preferred for simple comparison over a parametric method Explain the general principles and process of performing a non-parametric ...
Time series is data collected over time, and statistical learning is a field of statistics and machine learning that develops algorithms to model and interpret this data. Together, they use ...
Non-parametric tests are used when standard assumptions are not available. These tests don’t rely on distributions, often using real-world data instead. These tests are simple to implement, allowing ...
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 ...