Vakken
Engels
Frans
Duits
Spaans
Nederlands
Grieks
Portugees
Italiaans
Latijn
Japans
Biologie
Aardrijkskunde
Natuur- en scheikunde
Wiskunde, rekenen
Economie
Geschiedenis
Eigen methodes
Alle vakken
Home
›
Alle vakken
›
Eigen methodes
›
Private
› 1 Numerical Bootstrapping
Helaas is de overhoormodule niet beschikbaar. Wel kun je deze lijst overhoren via StudyGo. Klik op 'Overhoren'
Private
1 Numerical Bootstrapping
Link voor email / website
Link naar overhoring, zonder bewerk/reactiemogelijkheid (ELO)
Open met deze code de oefening in miniTeach
Twitter
Facebook
Google+
LinkedIn
What does bootstrapping allow? = Allows assigning measures of accuracy to sample estimates To what does bootstrapping refer? = Refers to any test or metric that relies on random sampling with replacement What is the idea? = Use the available data set to perform Monte Carlo simulations What is the principle? = Approximate the sampling distribution by simulating from a good model of the data, and treating the simulated data just like the real data Why bootstrap sampling? = We do not know the confidence interval or distribution of the statistic. Bootstrap gives more detail on the distribution or probability of this statistic. We can obtain the small sample distribution of the estimate of the statistic When do we bootstrap? = Theoretical distribution of a statistic is complicated or unknown. Property of bootstrapping? = It is distribution-independent. Why does bootstrapping work? = If the sample is a good approximation of the population, bootstrapping will give a good approximation of the sample distribution. What is assumed in residual bootstrap? = The regressors are fixed, only source of randomness are the errors. What is assumed in pairwise bootstrap? = Regressors are random. Other name for residual bootstrap? = Regression model. Other name for pairwise residual? = Correlation model. When to use block bootstrap? = When there is correlation in y and errors. What happens if L is too small? = It will corrupt the dependence structure, increasing bias. What happens if L is too large? = It will give the method high variance and consequent inaccuracy. What happens in parametric bootstrap? = Sample from the estimated distribution function of the data or residuals Difference between Monte Carlo and bootstrap? = Distribution must be known in Monte Carlo. For bootstrap, only sample is needed.
Ingezonden op 16-04-2017 - 399x bekeken.
Nog niet genoeg stemmen voor waardering: geef je mening!
voting system
1
2
3
4
5
Maak gratis account aan
Toon volledig menu
Door deze site te gebruiken, ga je akkoord met het gebruik van cookies voor analytische doeleinden, gepersonaliseerde inhoud en advertenties.
Meer informatie.
Overhoor en verbeter je talenkennis op woordjesleren.nl. De grootste verzameling van Franse, Engelse, Duitse en anderstalige oefeningen. Naast talen zijn ook andere vakken beschikbaar, zoals biologie, geschiedenis en aardrijkskunde!