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Estimating bivariate distributions assuming some form of dependence

Albers, C. (1998) Estimating bivariate distributions assuming some form of dependence. Master's Thesis / Essay, Mathematics.

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Abstract

Let (X1, Y1),..., (Xn,Yn) be an independent random sample from a bivariate population with distribution H. The stochastic variables X and Y are assumed to be (positively) associated in some way. To incorporate this assumption, various mathematical-statistical definitions can be used. We prefer the concept of (positive) quadrant dependence. This thesis contains various methods for estimating the distribution function H(x, y). Two semiparametric methods are developed and a nonparametric method is discussed. The results are not very promising: though those of the semiparametric methods display various similarities, they are considerably different. This might suggest that samples of size 50 are too small to arrive at acceptable estimates, unless restrictive assumptions are imposed.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Mathematics
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:29
Last Modified: 15 Feb 2018 07:29
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/8655

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