Search results for "nonparametric estimation"
showing 10 items of 11 documents
Space-time Point Processes semi-parametric estimation with predictive measure information
2014
In this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing nonparametric and parametric approaches. The method accounts simultaneously for the estimation of the different model components, applying a forward predictive likelihood estimation approach to semi-parametric models.
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…
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.
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…
Labor Productivity Growth: Disentangling Technology and Capital Accumulation
2014
We adopt a counterfactual approach to decompose labor productivity growth into growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total Factor Productivity (TFP). We bring the decomposition to the data using international countrysectoral information spanning from the 1960s to the 2000s and a nonparametric generalized kernel method, which enables us to estimate the production function allowing for heterogeneity across all relevant dimensions: countries, sectors and time. As well as documenting substantial heterogeneity across countries and sectors, we nd average TEP to account for about 44% of labor productivity growth and TEP gaps with respect to the…
Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially o…
2014
In the near future, millions of load curves measuring the electricity consumption of French households in small time grids (probably half hours) will be available. All these collected load curves represent a huge amount of information which could be exploited using survey sampling techniques. In particular, the total consumption of a specific cus- tomer group (for example all the customers of an electricity supplier) could be estimated using unequal probability random sampling methods. Unfortunately, data collection may undergo technical problems resulting in missing values. In this paper we study a new estimation method for the mean curve in the presence of missing values which consists in…
Nonparametric estimation of quantile versions of the Lorenz curve
2018
Estimators of quantile versions of the Lorenz curve are proposed. The pointwise consistency and asymptotic normality of the estimators is proved. The efficiency of the estimators is also studied in simulations
Alternated estimation in semi-parametric space-time branching-type point processes with application to seismic catalogs
2014
An estimation approach for the semi-param-etric intensity function of a class of space-time point processes 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 an offspring is therefore estimated.
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…
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…