Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data

• URL: http://arxiv.org/abs/2412.14946v1
• Date: Thu, 19 Dec 2024 15:26:55 GMT
• Title: Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data
• Authors: Yong Chen Goh, Wuu Kuang Soh, Andrew C. Parnell, Keefe Murphy,
• Abstract summary: Dealing with missing data poses significant challenges in predictive analysis. In cases where the data are missing not at random, jointly modeling the data and missing data indicators is essential. We propose two methods under a selection model framework for handling data with missingness.
• Score: 0.0
• License:
• Abstract: Dealing with missing data poses significant challenges in predictive analysis, often leading to biased conclusions when oversimplified assumptions about the missing data process are made. In cases where the data are missing not at random (MNAR), jointly modeling the data and missing data indicators is essential. Motivated by a real data application with partially missing multivariate outcomes related to leaf photosynthetic traits and several environmental covariates, we propose two methods under a selection model framework for handling data with missingness in the response variables suitable for recovering various missingness mechanisms. Both approaches use a multivariate extension of Bayesian additive regression trees (BART) to flexibly model the outcomes. The first approach simultaneously uses a probit regression model to jointly model the missingness. In scenarios where the relationship between the missingness and the data is more complex or non-linear, we propose a second approach using a probit BART model to characterize the missing data process, thereby employing two BART models simultaneously. Both models also effectively handle ignorable covariate missingness. The efficacy of both models compared to existing missing data approaches is demonstrated through extensive simulations, in both univariate and multivariate settings, and through the aforementioned application to the leaf photosynthetic trait data.

Related papers

• Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models [0.0]
  Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a sequential selection method that identifies critically important information within a dataset. We find these instabilities are a result of the complexity of the underlying map and linked to extreme events and heavy tails.
  arXiv  Detail & Related papers  (2022-08-27T19:43:53Z)
• Sharing pattern submodels for prediction with missing values [12.981974894538668]
  Missing values are unavoidable in many applications of machine learning and present challenges both during training and at test time. We propose an alternative approach, called sharing pattern submodels, which i) makes predictions robust to missing values at test time, ii) maintains or improves the predictive power of pattern submodels andiii) has a short description, enabling improved interpretability.
  arXiv  Detail & Related papers  (2022-06-22T15:09:40Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Variable selection with missing data in both covariates and outcomes: Imputation and machine learning [1.0333430439241666]
  The missing data issue is ubiquitous in health studies. Machine learning methods weaken parametric assumptions. XGBoost and BART have the overall best performance across various settings.
  arXiv  Detail & Related papers  (2021-04-06T20:18:29Z)
• Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness [0.0]
  We propose a variational autoencoder architecture to model both ignorable and nonignorable missing data. Our model explicitly learns to cluster the missing data into missingness pattern sets based on the observed data and missingness masks. Our setup trades off the characteristics of ignorable and nonignorable missingness and can thus be applied to data of both types.
  arXiv  Detail & Related papers  (2021-03-05T08:21:35Z)
• A Hypergradient Approach to Robust Regression without Correspondence [85.49775273716503]
  We consider a variant of regression problem, where the correspondence between input and output data is not available. Most existing methods are only applicable when the sample size is small. We propose a new computational framework -- ROBOT -- for the shuffled regression problem.
  arXiv  Detail & Related papers  (2020-11-30T21:47:38Z)
• Variational Bayesian Unlearning [54.26984662139516]
  We study the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We show that it is equivalent to minimizing an evidence upper bound which trades off between fully unlearning from erased data vs. not entirely forgetting the posterior belief. In model training with VI, only an approximate (instead of exact) posterior belief given the full data can be obtained, which makes unlearning even more challenging.
  arXiv  Detail & Related papers  (2020-10-24T11:53:00Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• Data from Model: Extracting Data from Non-robust and Robust Models [83.60161052867534]
  This work explores the reverse process of generating data from a model, attempting to reveal the relationship between the data and the model. We repeat the process of Data to Model (DtM) and Data from Model (DfM) in sequence and explore the loss of feature mapping information. Our results show that the accuracy drop is limited even after multiple sequences of DtM and DfM, especially for robust models.
  arXiv  Detail & Related papers  (2020-07-13T05:27:48Z)
• not-MIWAE: Deep Generative Modelling with Missing not at Random Data [21.977065542645082]
  We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
  arXiv  Detail & Related papers  (2020-06-23T10:06:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.