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dc.contributor.authorOtneim, Håkon
dc.contributor.authorTjøstheim, Dag
dc.date.accessioned2016-12-07T15:22:07Z
dc.date.available2016-12-07T15:22:07Z
dc.date.issued2016-12-07
dc.identifier.issn1500-4066
dc.identifier.urihttp://hdl.handle.net/11250/2424564
dc.description.abstractLet X = (X1,...,Xp) be a stochastic vector having joint density function fX(x) with partitions X1 = (X1,...,Xk) and X2 = (Xk+1,...,Xp). A new method for estimating the conditional density function of X1 given X2 is presented. It is based on locally Gaussian approximations, but simplified in order to tackle the curse of dimensionality in multivariate applications, where both response and explanatory variables can be vectors. We compare our method to some available competitors, and the error of approximation is shown to be small in a series of examples using real and simulated data, and the estimator is shown to be particularly robust against noise caused by independent variables. We also present examples of practical applications of our conditional density estimator in the analysis of time series. Typical values for k in our examples are 1 and 2, and we include simulation experiments with values of p up to 6. Large sample theory is established under a strong mixing condition.nb_NO
dc.language.isoengnb_NO
dc.publisherFORnb_NO
dc.relation.ispartofseriesDiscussion paper;22/16
dc.subjectConditional density estimationnb_NO
dc.subjectlocal likelihoodnb_NO
dc.subjectmultivariate datanb_NO
dc.subjectcrossvalidationnb_NO
dc.titleNon-parametric estimation of conditional densities: A new methodnb_NO
dc.typeWorking papernb_NO
dc.source.pagenumber25nb_NO


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