Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Estimation of marginal excess moments for Weibull-type distributions
by
Qin, Jing
, Goegebeur, Yuri
, Guillou, Armelle
in
Bivariate analysis
/ Extreme values
/ Random variables
/ Wind speed
2024
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Estimation of marginal excess moments for Weibull-type distributions
by
Qin, Jing
, Goegebeur, Yuri
, Guillou, Armelle
in
Bivariate analysis
/ Extreme values
/ Random variables
/ Wind speed
2024
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Estimation of marginal excess moments for Weibull-type distributions
Journal Article
Estimation of marginal excess moments for Weibull-type distributions
2024
Request Book From Autostore
and Choose the Collection Method
Overview
We consider the estimation of the marginal excess moment (MEM), which is defined for a random vector (X, Y) and a parameter β>0 as E[(X-QX(1-p))+β|Y>QY(1-p)] provided E|X|β<∞, and where y+:=max(0,y), QX and QY are the quantile functions of X and Y respectively, and p∈(0,1). Our interest is in the situation where the random variable X is of Weibull-type while the distribution of Y is kept general, the extreme dependence structure of (X, Y) converges to that of a bivariate extreme value distribution, and we let p↓0 as the sample size n→∞. By using extreme value arguments we introduce an estimator for the marginal excess moment and we derive its limiting distribution. The finite sample properties of the proposed estimator are evaluated with a simulation study and the practical applicability is illustrated on a dataset of wave heights and wind speeds.
Publisher
Springer Nature B.V
Subject
This website uses cookies to ensure you get the best experience on our website.