Missing Data Problems

Missing data problems are often best tackled by taking into consideration specificities of the data structure and data generating process. In this doctoral dissertation, I present a thorough study of two specific problems. The first problem is one of regression analysis with misaligned data; that is, when the geographic location of the dependent variable and that of some independent variable do not coincide. The misaligned independent variable is rainfall, and it can be successfully modeled as a Gaussian random field, which makes identification possible. In the second problem, the missing independent variable a categorical. In that case, I am able to train a machine learning algorithm which predicts the missing variable. A common theme throughout is the tension between efficiency and robustness. Both missing data problems studied herein arise from the merging of separate sources of data.Economic

An Adversarial Approach to Structural Estimation

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.Comment: 58 pages, 3 tables, 4 figure

The Maximum Likelihood Threshold of a Path Diagram

Linear structural equation models postulate noisy linear relationships between variables of interest. Each model corresponds to a path diagram, which is a mixed graph with directed edges that encode the domains of the linear functions and bidirected edges that indicate possible correlations among noise terms. Using this graphical representation, we determine the maximum likelihood threshold, that is, the minimum sample size at which the likelihood function of a Gaussian structural equation model is almost surely bounded. Our result allows the model to have feedback loops and is based on decomposing the path diagram with respect to the connected components of its bidirected part. We also prove that if the sample size is below the threshold, then the likelihood function is almost surely unbounded. Our work clarifies, in particular, that standard likelihood inference is applicable to sparse high-dimensional models even if they feature feedback loops

L'hypnose : une nouvelle avenue à considérer pour les professionnels de la réadaptation

"La douleur est un phénomène présent pour un grand nombre de personnes vues en ergothérapie (Hesselstrand, Samuelsson et Liedberg, 2015). Malgré les outils présentement à notre disposition, la gestion de la douleur chronique reste un défi majeur pour les ergothérapeutes et autres professionnels de la santé. Dans d’autres domaines, les professionnels utilisent une modalité de traitement qui est encore peu connue de notre profession. Saviez-vous qu’une méta-analyse a permis de conclure que l’utilisation de l’hypnose est une technique de gestion de la douleur efficace (Montgomery, DuHamel et Redd, 2000)?" [...].

Degrees of Freedom and Information Criteria for the Synthetic Control Method

We provide an analytical characterization of the model flexibility of the synthetic control method (SCM) in the familiar form of degrees of freedom. We obtain estimable information criteria. These may be used to circumvent cross-validation when selecting either the weighting matrix in the SCM with covariates, or the tuning parameter in model averaging or penalized variants of SCM. We assess the impact of car license rationing in Tianjin and make a novel use of SCM; while a natural match is available, it and other donors are noisy, inviting the use of SCM to average over approximately matching donors. The very large number of candidate donors calls for model averaging or penalized variants of SCM and, with short pre-treatment series, model selection per information criteria outperforms that per cross-validation

An adversarial approach to structural estimation

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly's saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich

Impact of Ultra-High Pressure Homogenization on the Structural Properties of Egg Yolk Granule

Ultra-high pressure homogenization (UHPH) is a promising method for destabilizing and potentially improving the techno-functionality of the egg yolk granule. This study’s objectives were to determine the impact of pressure level (50, 175 and 300 MPa) and number of passes (1 and 4) on the physico-chemical and structural properties of egg yolk granule and its subsequent fractions. UHPH induced restructuration of the granule through the formation of a large protein network, without impacting the proximate composition and protein profile in a single pass of up to 300 MPa. In addition, UHPH reduced the particle size distribution up to 175 MPa, to eventually form larger particles through enhanced protein–protein interactions at 300 MPa. Phosvitin, apovitellenin and apolipoprotein-B were specifically involved in these interactions. Overall, egg yolk granule remains highly stable during UHPH treatment. However, more investigations are needed to characterize the resulting protein network and to evaluate the techno-functional properties of UHPH-treated granule