Mauricio Sadinle



My methodological research mainly focuses on:
  1. Record linkage techniques to combine datafiles that contain information on overlapping sets of individuals but lack unique identifiers.
  2. Nonignorable missing data modeling, and the usage of auxiliary information to identify nonignorable missing data mechanisms.
I also have experience working with social network models for valued ties, capture-recapture models in the context of human rights violations, and set-valued classifiers that output sets of plausible labels for ambiguous sample points.


Selected Publications

You can find the full list of my publications in my CV, on Google Scholar, or on arXiv.

Missing Data

Sequentially Additive Nonignorable Missing Data Modeling Using Auxiliary Marginal Information
Mauricio Sadinle and Jerome P. Reiter
Accepted in Biometrika (2019+) [arXiv]

Sequential Identification of Nonignorable Missing Data Mechanisms
Mauricio Sadinle and Jerome P. Reiter
Statistica Sinica (2018), 28(4), 1741-1759. Special Issue on Data Missing Not At Random. [arXiv]

Itemwise Conditionally Independent Nonresponse Modeling for Incomplete Multivariate Data
Mauricio Sadinle and Jerome P. Reiter
Biometrika (2017), 104(1):207-220 [arXiv]

Record Linkage

Bayesian Propagation of Record Linkage Uncertainty into Population Size Estimation of Human Rights Violations
Mauricio Sadinle
Annals of Applied Statistics (2018), 12(2):1013-1038. Special Section in Memory of Stephen E. Fienberg. [arXiv]

Bayesian Estimation of Bipartite Matchings for Record Linkage
Mauricio Sadinle
Journal of the American Statistical Association (2017), 112(518):600-612. [arXiv]

Detecting Duplicates in a Homicide Registry Using a Bayesian Partitioning Approach
Mauricio Sadinle
Annals of Applied Statistics (2014), 8(4):2404-2434. [arXiv]

A Generalized Fellegi-Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems
Mauricio Sadinle and Stephen E. Fienberg
Journal of the American Statistical Association (2013), 108(502):385-397. [arXiv]

Set-Valued Classifiers

Least Ambiguous Set-Valued Classifiers with Bounded Error Levels
Mauricio Sadinle, Jing Lei, and Larry Wasserman
To appear in Journal of the American Statistical Association (2019). [arXiv]