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dc.identifier.citationZammit, M.V. (2017). Nonparametric density estimation concerning social benefits in Malta (Bachelor's dissertation).en_GB
dc.description.abstractNonparametric density estimation involves obtaining an estimate for the unknown probability density function given a sample of size 𝑛 but without having any prior knowledge on the form of the function. We shall consider the wavelet and kernel density estimation methods to obtain the estimate of the unknown density function. Since this density function is unknown, we shall work in an infinite dimensional space. However, in practice we are not able to work in this space, thus we shall use the method of sieves to approximate our infinite dimensional space with a sequence of finite dimensional spaces. We shall use wavelets as our orthonormal bases which will then be used to obtain the required wavelet density estimator. On the other hand, the kernel density estimator is based on the choice on the smoothing parameter and the kernel function. We shall compare the two nonparmateric methods by applying both techniques to a sample of Maltese individuals who were entitled to receive different types of social benefits.en_GB
dc.subjectNonparametric statisticsen_GB
dc.subjectEstimation theoryen_GB
dc.subjectDistribution (Probability theory)en_GB
dc.subjectPublic welfare -- Maltaen_GB
dc.titleNonparametric density estimation concerning social benefits in Maltaen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Science. Department of Statistics and Operations Researchen_GB
dc.contributor.creatorZammit, Michaela Vania (2017)-
Appears in Collections:Dissertations - FacSci - 2017
Dissertations - FacSciSOR - 2017

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