Extreme Models

Extreme Models for Climate Change

There are various extreme value modelling methods available. Most of these models assume stationary conditions, but climate change can cause shifts or non-stationarity in extremes. To account for this effect, non-stationary GEV-models for significant wave height are developed. They are designed to block maxima in wave data and are then investigated over a period.

Maximum likelihood estimation has been a flexible method for estimating extreme behavior. It has had its limitations, especially when working with small samples. A better fitting procedure called probability weighted moments has been developed. This method improves upon maximum likelihood estimation. It can be used for large or small samples depending on the data.

The Pareto distribution was first explored by Italian economist Vilfredo Pareto in 1848. It has a slower exponential rate of decay than a normal distribution. It is also better suited for modeling distributions with fat tails. Furthermore, a generalized Pareto sequence converges to a generalized Pareto distribution, even when the claim random variables are different.

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