In: Journal of computational science, 2021, vol. 53, p. 13
The ℓ1-regularized Gaussian maximum likelihood method is a common approach for sparse precision matrix estimation, but one that poses a computational challenge for high-dimensional datasets. We present a novel ℓ1- regularized maximum likelihood method for performant large-scale sparse precision matrix estimation utilizing the block structures in the underlying computations. We identify the...
|
In: Journal für die reine und angewandte Mathematik (Crelles Journal), 2017, vol. 2017, no. 725, p. 217-234
|
In: Biometrika, 2018, vol. 105, no. 3, p. 575-592
|
In: Information and Inference: A Journal of the IMA, 2017, vol. 6, no. 3, p. 246-283
|
Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO008.
Fundamental tasks in multivariate and numerical analysis, such as sparse precision matrix estimation via graphical lasso and function approximation, are formulated in ever-increasing dimensions. Consequently, this results in a significant increase in the computational demand that quickly renders standard solution methods intractable. With this motivation, we present two scalable algorithms that...
|
In: Journal of Econometric Methods, 2019, vol. 8, no. 1, p. 1-20
Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the...
|
In: Journal of Business and Economic Statistics, 2019, vol. 37, no. 4, p. 736-748
This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one- dimensional nonparametric regression, irrespective of the...
|
Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020ECO008.
My doctoral thesis examines the relationships among the degree of financial market integration and the pricing of different classes of assets. The first chapter provides a theoretical framework that uncovers in a model-free way the relationship between international stochastic discount factors (SDFs), stochastic wedges, and financial market structures. Exchange rates are in general different...
|
Thèse de doctorat : Università della Svizzera italiana, 2020 ; 2020ECO007.
Empirical indicators of sentiment are commonly employed in the economic literature while a precise understanding of what is sentiment is still missing. Exploring the links among the most popular proxies of sentiment, fear and uncertainty this paper aims to fill this gap. We show how fear and sentiment are specular in their predictive power in relation to the aggregate market and to...
|
In: Journal of Behavioral Finance, 2020, vol. 21, no. 1, p. 78-102
Structured equity-linked products hold a strong position in the asset universe in Europe although they are often considered to be overly complex. Their risk and return profi le is typically presented by simple payoff diagrams and verbal descriptions. We propose to complement the payoff diagrams with information on the payoff's probability distribution and study different presentation formats...
|