Statistical Methods for Big Data

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http://hdl.handle.net/2003/40304

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Now showing 1 - 3 of 3
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    Introducing LASSO-type penalisation to generalised joint regression modelling for count data
    (2021-11-12) van der Wurp, Hendrik; Groll, Andreas
    In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the R package GJRM by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data.
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    Flexible instrumental variable distributional regression
    (2020-08-16) Briseño Sanchez, Guillermo; Hohberg, Maike; Groll, Andreas; Kneib, Thomas
    We tackle two limitations of standard instrumental variable regression in experimen- tal and observational studies: restricted estimation to the conditional mean of the outcome and the assumption of a linear relationship between regressors and outcome. More flexible regres- sion approaches that solve these limitations have already been developed but have not yet been adopted in causality analysis. The paper develops an instrumental variable estimation proce- dure building on the framework of generalized additive models for location, scale and shape. This enables modelling all distributional parameters of potentially complex response distribu- tions and non-linear relationships between the explanatory variables, instrument and outcome. The approach shows good performance in simulations and is applied to a study that estimates the effect of rural electrification on the employment of females and males in the South African province of KwaZulu-Natal. We find positive marginal effects for the mean for employment of females rates, negative effects for employment of males and a reduced conditional standard deviation for both, indicating homogenization in employment rates due to the electrification pro- gramme. Although none of the effects are statistically significant, the application demonstrates the potentials of using generalized additive models for location, scale and shape in instrumental variable regression for both to account for endogeneity and to estimate treatment effects beyond the mean.