Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

3-12-2019

Journal or Book Title

arxiv

Abstract

We propose estimation methods for unnormalized models with missing data. The key concept is to combine a modern imputation technique with estimators for unnormalized models including noise contrastive estimation and score matching. Further, we derive asymptotic distributions of the proposed estimators and construct the confidence intervals. The application to truncated Gaussian graphical models with missing data shows the validity of the proposed methods.

Comments

This pre-print is made available through arxiv: https://arxiv.org/abs/1903.03630.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

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