arXiv subjects

Daniel Suen

Publications and source records attributed to Daniel Suen.

7 recordsLinked to original sources

arXiv2026

Modeling Multivariate Missingness with Tree Graphs and Conjugate Odds

In this paper, we analyze a specific class of missing not at random (MNAR) assumptions called tree graphs, extending upon the work of pattern graphs. We build off previous work by introducing the idea of a conjugate odds family in which certain parametric models on the selection odds can preserve the data distribution family across all missing data patterns. Under a conjugate odds family and a tree graph assumption, we are able to model the full data distribution elegantly in the sense that for the observed data, we obtain a model that is conjugate from the complete-data, and for the missing entries, we create a simple imputation model. In addition, we investigate the problem of graph selection, sensitivity analysis, and statistical inference. Using both simulations and real data, we illustrate the applicability of our method.

stat.ME↗

arXiv2025

Masking criteria for selecting an imputation model

The masking-one-out (MOO) procedure, masking an observed entry and comparing it versus its imputed values, is a very common procedure for comparing imputation models. We study the optimum of this procedure and generalize it to a missing data assumption and establish the corresponding semi-parametric efficiency theory. However, MOO is a measure of prediction accuracy, which is not ideal for evaluating an imputation model. To address this issue, we introduce three modified MOO criteria, based on rank transformation, energy distance, and likelihood principle, that allow us to select an imputation model that properly account for the stochastic nature of data. The likelihood approach further enables an elegant framework of learning an imputation model from the data and we derive its statistical and computational learning theories as well as consistency of BIC model selection. We also show how MOO is related to the missing-at-random assumption. Finally, we introduce the prediction-imputation diagram, a two-dimensional diagram visually comparing both the prediction and imputation utilities for various imputation models.

stat.ME↗

arXiv2023

Modeling Missing at Random Neuropsychological Test Scores Using a Mixture of Binomial Product Experts

Multivariate bounded discrete data arises in many fields. In the setting of dementia studies, such data is collected when individuals complete neuropsychological tests. We outline a modeling and inference procedure that can model the joint distribution conditional on baseline covariates, leveraging previous work on mixtures of experts and latent class models. Furthermore, we illustrate how the work can be extended when the outcome data is missing at random using a nested EM algorithm. The proposed model can incorporate covariate information and perform imputation and clustering. We apply our model on simulated data and an Alzheimer's disease data set.

stat.ME↗

arXiv2021

Multistage Estimators for Missing Covariates and Incomplete Outcomes

We study problems with multiple missing covariates and partially observed responses. We develop a new framework to handle complex missing covariate scenarios via inverse probability weighting, regression adjustment, and a multiply-robust procedure. We apply our framework to three classical problems: the Cox model from survival analysis, missing response, and binary treatment from causal inference. We also discuss how to handle missing covariates in these scenarios, and develop associated identifying theories and asymptotic theories. We apply our procedure to simulations and an Alzheimer's disease dataset and obtain meaningful results.

stat.ME↗

arXiv2021

Possibilistic Fuzzy Local Information C-Means with Automated Feature Selection for Seafloor Segmentation

The Possibilistic Fuzzy Local Information C-Means (PFLICM) method is presented as a technique to segment side-look synthetic aperture sonar (SAS) imagery into distinct regions of the sea-floor. In this work, we investigate and present the results of an automated feature selection approach for SAS image segmentation. The chosen features and resulting segmentation from the image will be assessed based on a select quantitative clustering validity criterion and the subset of the features that reach a desired threshold will be used for the segmentation process.

cs.CV↗

arXiv2019

Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation

Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. The Possibilistic Fuzzy Local Information C-Means (PFLICM) algorithm has been previously applied to segment SAS imagery. Additionally, the Possibilistic K-Nearest Neighbors (PKNN) algorithm has been used in other domains such as landmine detection and hyperspectral imagery. In this paper, we compare the segmentation performance of a semi-supervised approach using PFLICM and a supervised method using Possibilistic K-NN. We include final segmentation results on multiple SAS images and a quantitative assessment of each algorithm.

eess.IV↗

arXiv2017

Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation

Side-look synthetic aperture sonar (SAS) can produce very high quality images of the sea-floor. When viewing this imagery, a human observer can often easily identify various sea-floor textures such as sand ripple, hard-packed sand, sea grass and rock. In this paper, we present the Possibilistic Fuzzy Local Information C-Means (PFLICM) approach to segment SAS imagery into sea-floor regions that exhibit these various natural textures. The proposed PFLICM method incorporates fuzzy and possibilistic clustering methods and leverages (local) spatial information to perform soft segmentation. Results are shown on several SAS scenes and compared to alternative segmentation approaches.

cs.CV↗