Extreme Value Theory
| Description: This quiz covers the fundamental concepts and applications of Extreme Value Theory (EVT). Test your understanding of EVT, including its distributions, properties, and applications in various fields. | |
| Number of Questions: 15 | |
| Created by: Aliensbrain Bot | |
| Tags: probability statistics extreme value theory gumbel distribution frechet distribution weibull distribution |
In Extreme Value Theory, the Generalized Extreme Value (GEV) distribution is a family of continuous probability distributions that model the behavior of ?
Which of the following distributions is a special case of the GEV distribution when the shape parameter is zero?
In EVT, the ? distribution is used to model the distribution of minima of random variables.
The ? distribution is a special case of the GEV distribution when the shape parameter is positive.
The ? distribution is a special case of the GEV distribution when the shape parameter is negative.
In EVT, the ? theorem states that the distribution of the maximum of a sequence of independent and identically distributed (i.i.d.) random variables converges to the GEV distribution under certain conditions.
Which of the following is a common application of EVT in ?
In ?, EVT is used to model the distribution of extreme weather events, such as floods, droughts, and hurricanes.
In ?, EVT is used to analyze and model the distribution of extreme loads and stresses on structures, such as bridges, buildings, and aircraft.
In ?, EVT is used to model the distribution of extreme financial events, such as stock market crashes and currency fluctuations.
The ? distribution is a special case of the GEV distribution when the shape parameter is equal to one.
In EVT, the ? plot is a graphical tool used to visualize and analyze the distribution of extreme values.
The ? distribution is a special case of the GEV distribution when the shape parameter is negative and the scale parameter is positive.
In EVT, the ? theorem states that the distribution of the minimum of a sequence of independent and identically distributed (i.i.d.) random variables converges to the GEV distribution under certain conditions.
The ? distribution is a special case of the GEV distribution when the shape parameter is positive and the scale parameter is positive.