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LASSO-type penalization in the framework of generalized additive models for location, scale and shape
Groll, Andreas; Hambuckers, Julien; Kneib, Thomas; … - 2018
models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high … dimensional data set-ups classical fitting procedures for the GAMLSS often become rather unstable and methods for variable … GAMLSS. It is designed for linear covariate effects and is based on L1-type penalties. The following three penalization …
Persistent link: https://www.econbiz.de/10011930753
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Vulnerability to poverty revisited : flexible modeling and better predictive performance
Hohberg, Maike; Landau, Katja; Kneib, Thomas; Klasen, … - 2017
This paper analyzes several modifications to improve a simple measure of vulnerability as expected poverty. Firstly, in order to model income, we apply distributional regression relating potentially each parameter of the conditional income distribution to the covariates. Secondly, we determine...
Persistent link: https://www.econbiz.de/10011743759
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Permutation tests on returns to scale and common production frontiers in nonparametric models
Rønn-Nielsen, Anders; Kronborg, Dorte; Asmild, Mette - 2022
Permutation techniques, where one recompute the test statistic over permutations of data, have a long history in statistics and have become increasingly useful as the availability of computational power has increased. Until now, no permutation tests for examining returns to scale assumptions,...
Persistent link: https://www.econbiz.de/10013338075
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A specification test for nonparametric instrumental variable regression
Gagliardini, Patrick (contributor);  … - 2007
We consider testing for correct specification of a nonparametric instrumental variable regression. In this ill-posed inverse problem setting, the test statistic is based on the empirical minimum distance criterion corresponding to the conditional moment restriction evaluated with a Tikhonov...
Persistent link: https://www.econbiz.de/10003550675
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Robust resampling methods for time series
Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2010 - This version: May 9, 2010
We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in...
Persistent link: https://www.econbiz.de/10003971115
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Smoothed influence function : another view at robust nonparametric regression
Tamine, Julien - 2001
In this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show...
Persistent link: https://www.econbiz.de/10009626684
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Counterfactual distributions in bivariate models : a conditional quantile approach
Alejo, Javier; Badaracco, Nicolás - In: Econometrics : open access journal 3 (2015) 4, pp. 719-732
This paper proposes a methodology to incorporate bivariate models in numerical computations of counterfactual distributions. The proposal is to extend the works of Machado and Mata (2005) and Melly (2005) using the grid method to generate pairs of random variables. This contribution allows...
Persistent link: https://www.econbiz.de/10011411683
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A multivariate hidden semi-Markov model of customer-multichannel engagement
Sikdar, Sharmistha; Hooker, Giles - 2019
In multichannel retailing, customers often use a mix of a firm's online and physical stores for their search and buy activities. We define the customer's “channel engagement” as a latent attitude or predisposition towards the firm's online and offline channels which dynamically transitions...
Persistent link: https://www.econbiz.de/10012244850
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Modelling of Left-Truncated Heavy-Tailed Data with Application to Catastrophe Bond Pricing
Giuricich, Mario - 2019
In this article, we build on Chernobai et al. [1]'s procedure for modelling left-truncated data via a compound non-homogeneous Poisson process. The contribution we make is that we modify the fitting process introduced so that it is systematically applicable in the context of data that is not...
Persistent link: https://www.econbiz.de/10012901830