Search results for "Parametric estimation"
showing 6 items of 16 documents
Parametric estimation of non-crossing quantile functions
2021
Quantile regression (QR) has gained popularity during the last decades, and is now considered a standard method by applied statisticians and practitioners in various fields. In this work, we applied QR to investigate climate change by analysing historical temperatures in the Arctic Circle. This approach proved very flexible and allowed to investigate the tails of the distribution, that correspond to extreme events. The presence of quantile crossing, however, prevented using the fitted model for prediction and extrapolation. In search of a possible solution, we first considered a different version of QR, in which the QR coefficients were described by parametric functions. This alleviated th…
Multivariate nonparametric estimation of the Pickands dependence function using Bernstein polynomials
2017
Abstract Many applications in risk analysis require the estimation of the dependence among multivariate maxima, especially in environmental sciences. Such dependence can be described by the Pickands dependence function of the underlying extreme-value copula. Here, a nonparametric estimator is constructed as the sample equivalent of a multivariate extension of the madogram. Shape constraints on the family of Pickands dependence functions are taken into account by means of a representation in terms of Bernstein polynomials. The large-sample theory of the estimator is developed and its finite-sample performance is evaluated with a simulation study. The approach is illustrated with a dataset of…
Forward likelihood-based predictive approach for space-time point processes
2011
Dealing with data from a space–time point process, the estimation of the conditional intensity function is a crucial issue even if a complete definition of a parametric model is not available. In particular, in case of exploratory contexts or if we want to assess the adequacy of a specific parametric model, some kind of nonparametric estimation procedure could be useful. Often, for these purposes kernel estimators are used and the estimation of the intensity function depends on the estimation of bandwidth parameters. In some fields, like for instance the seismological one, predictive properties of the estimated intensity function are pursued. Since a direct ML approach cannot be used, we pr…
Goodness-of-fit tests for parametric excess hazard rate models with covariates
2017
In this paper we propose a general methodology for testing the null hypothesis that an excess hazard rate model, with or without covariates, belongs to a parametric family. Estimating the excess hazard rate function parametrically through the maximum likelihood method and non-parametrically (or semi-parametrically) we build a discrepancy process which is shown to be asymptotically Gaussian under the null hypothesis. Based on this result we are able to build some statistical tests in order to decide wether or not the null hypothesis is acceptable. We illustrate our results by the construction of chi-square tests which the behavior is studied through a Monte-Carlo study. Then the testing proc…
Estimating regional differences in returns to education when schooling and location are determined endogenously
2010
While the growing supply of university skills is known to have agglomerated towards the large centers in Finland, there is no research knowledge available on the development of regional demands. This paper attempts to fill this gap by analyzing regional variation in the private-sector return to university education in Finland for the period 1970 - 2004. In the analysis, we focus on studying 1) whether there are differences in the return to university between different region types, and 2) to what extent can these differences - if they exist - be explained by differences in regional skill supply and unemployment. For the econometric analysis, we use a large register-based dataset constructed…
Mixed estimation technique in semi-parametric space-time point processes for earthquake description
2013
An estimation approach for the semi-parametric intensity function of a particular space-time point process is introduced. In particular we want to account for the estimation of parametric and nonparametric components simultaneously, applying a forward predictive likelihood to semi-parametric models. For each event, the probability of being a background event or one belonging to a seismic sequence is therefore estimated.