TY  - JOUR
T1  - Sequential identification of nonignorable missing data mechanisms
JF  - Statistica Sinica
Y1  - Submitted
A1  - Mauricio Sadinle
A1  - Jerome P. Reiter
KW  - Identification
KW  - Missing not at random
KW  - Non-parametric saturated
KW  - Partial ignorability
KW  - Sensitivity analysis
AB  - With nonignorable missing data, likelihood-based inference should be based on the joint distribution of the study variables and their missingness indicators. These joint models cannot be estimated from the data alone, thus requiring the analyst to impose restrictions that make the models uniquely obtainable from the distribution of the observed data. We present an approach for constructing classes of identifiable nonignorable missing data models. The main idea is to use a sequence of carefully set up identifying assumptions, whereby we specify potentially different missingness mechanisms for different blocks of variables. We show that the procedure results in models with the desirable property of being non-parametric saturated.
ER  - 

TY  - JOUR
T1  - Bayesian estimation of bipartite matchings for record linkage
JF  - Journal of the American Statistical Association
Y1  - 2017
A1  - Mauricio Sadinle
KW  - Assignment problem
KW  - Bayes estimate
KW  - Data matching
KW  - Fellegi-Sunter decision rule
KW  - Mixture model
KW  - Rejection option
AB  - The bipartite record linkage task consists of merging two disparate datafiles containing  information on two overlapping sets of entities. This is non-trivial in the absence  of unique identifiers and it is important for a wide variety of applications given  that it needs to be solved whenever we have to combine information from different  sources. Most statistical techniques currently used for record linkage are derived from  a seminal paper by Fellegi and Sunter (1969). These techniques usually assume independence  in the matching statuses of record pairs to derive estimation procedures  and optimal point estimators. We argue that this independence assumption is unreasonable  and instead target a bipartite matching between the two datafiles as our  parameter of interest. Bayesian implementations allow us to quantify uncertainty on  the matching decisions and derive a variety of point estimators using different loss  functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite  matching to be left unresolved. We evaluate our approach to record linkage  using a variety of challenging scenarios and show that it outperforms the traditional  methodology. We illustrate the advantages of our methods merging two datafiles on  casualties from the civil war of El Salvador.
VL  - 112
UR  - http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2016.1148612
IS  - 518
ER  - 

TY  - RPRT
T1  - A Generalized Fellegi-Sunter Framework for Multiple Record Linkage with Application to Homicide Records Systems
Y1  - 2012
A1  - Mauricio Sadinle
A1  - Stephen E. Fienberg
JF  - arXiv
UR  - https://arxiv.org/abs/1205.3217
ER  - 

TY  - CONF
T1  - Logit-Based Confidence Intervals for Single Capture-Recapture Estimation
T2  - American Statistical Association Pittsburgh Chapter Banquet
Y1  - 2012
A1  - Mauricio Sadinle
JF  - American Statistical Association Pittsburgh Chapter Banquet
CY  - Pittsburgh, PA
N1  - April 9, 2012
ER  - 

TY  - CONF
T1  - MulFiles Record Linkage Using a Generalized Fellegi-Sunter Framework
T2  - Conference Presentation Classification Society Annual Meeting, Carnegie Mellon University
Y1  - 2012
A1  - Mauricio Sadinle
JF  - Conference Presentation Classification Society Annual Meeting, Carnegie Mellon University
ER  - 

TY  - JOUR
T1  - Bayesian estimation of bipartite matchings for record linkage
JF  - Journal of the American Statistical Association
Y1  - 0
A1  - Mauricio Sadinle
AB  - The bipartite record linkage task consists of merging two disparate datafiles containing information on two overlapping sets of entities. This is non-trivial in the absence of unique identifiers and it is important for a wide variety of applications given that it needs to be solved whenever we have to combine information from different sources. Most statistical techniques currently used for record linkage are derived from a seminal paper by Fellegi and Sunter (1969). These techniques usually assume independence in the matching statuses of record pairs to derive estimation procedures and optimal point estimators. We argue that this independence assumption is unreasonable and instead target a bipartite matching between the two datafiles as our parameter of interest. Bayesian implementations allow us to quantify uncertainty on the matching decisions and derive a variety of point estimators using different loss functions. We propose partial Bayes estimates that allow uncertain parts of the bipartite matching to be left unresolved. We evaluate our approach to record linkage using a variety of challenging scenarios and show that it outperforms the traditional methodology. We illustrate the advantages of our methods merging two datafiles on casualties from the civil war of El Salvador.
ER  -