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Working paper
Semiparametric Distribution Regression with Instruments and Monotonicity
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Bivariate Distribution Regression with Application to Insurance Data
In: Insurance: Mathematics and Economics, Forthcoming
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Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes
In: IZA Discussion Paper No. 15236
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Using Distribution Regression Difference-in-Differences to Evaluate the Effects of a Minimum Wage Introduction on the Distribution of Hourly Wages and Hours Worked
In: IZA Discussion Paper No. 15534
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Using Post-Regularization Distribution Regression to Measure the Effects of a Minimum Wage on Hourly Wages, Hours Worked and Monthly Earnings
In: IZA Discussion Paper No. 16894
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Almost opposite regression dependence in bivariate distributions
In: Statistical papers, Band 56, Heft 4, S. 1033-1039
ISSN: 1613-9798
Asymptotic distribution of bandwidth selectors in kernel regression estimation
In: Statistical papers, Band 35, Heft 1, S. 17-26
ISSN: 1613-9798
Rank Regressions, Wage Distributions, and the Gender Gap
In: The journal of human resources, Band 33, Heft 3, S. 610
ISSN: 1548-8004
Quantile Regressions: Estimating Moments of the Stock Return Distribution
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Robust mixture regression via an asymmetric exponential power distribution
In: Communications in statistics. Simulation and computation, Band 53, Heft 5, S. 2486-2497
ISSN: 1532-4141
Recovering Conditional Return Distributions by Regression: Estimation and Applications
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Working paper
Identification of power distribution mixtures through regression of exponentials
In: Statistical papers, Band 54, Heft 1, S. 227-241
ISSN: 1613-9798
An informative prior distribution on functions with application to functional regression
In: Statistica Neerlandica: journal of the Netherlands Society for Statistics and Operations Research, Band 78, Heft 2, S. 357-373
ISSN: 1467-9574
We provide a prior distribution for a functional parameter so that its trajectories are smooth and vanish on a given subset. This distribution can be interpreted as the distribution of an initial Gaussian process conditioned to be zero on a given subset. Precisely, we show that the initial Gaussian process is the sum of the conditioned process and an independent process with probability one and that all the processes have the same almost sure regularity. This prior distribution is use to provide an interpretable estimate of the coefficient function in the linear scalar‐on‐function regression; by interpretable, we mean a smooth function that may possibly be zero on some intervals. We apply our model in a simulation and real case studies with two different priors for the null region of the coefficient function. In one case, the null region is known to be an unknown single interval. In the other case, it can be any unknown unions of intervals.
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