Evaluation of Missing Data Analytical Techniques in Longitudinal Research: Traditional and Machine Learning Approaches
- URL: http://arxiv.org/abs/2406.13814v1
- Date: Wed, 19 Jun 2024 20:20:30 GMT
- Title: Evaluation of Missing Data Analytical Techniques in Longitudinal Research: Traditional and Machine Learning Approaches
- Authors: Dandan Tang, Xin Tong,
- Abstract summary: This study utilizes Monte Carlo simulations to assess and compare the effectiveness of six analytical techniques for missing data within the growth curve modeling framework.
We investigate the influence of sample size, missing data rate, missing data mechanism, and data distribution on the accuracy and efficiency of model estimation.
- Score: 11.048092826888412
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Missing Not at Random (MNAR) and nonnormal data are challenging to handle. Traditional missing data analytical techniques such as full information maximum likelihood estimation (FIML) may fail with nonnormal data as they are built on normal distribution assumptions. Two-Stage Robust Estimation (TSRE) does manage nonnormal data, but both FIML and TSRE are less explored in longitudinal studies under MNAR conditions with nonnormal distributions. Unlike traditional statistical approaches, machine learning approaches do not require distributional assumptions about the data. More importantly, they have shown promise for MNAR data; however, their application in longitudinal studies, addressing both Missing at Random (MAR) and MNAR scenarios, is also underexplored. This study utilizes Monte Carlo simulations to assess and compare the effectiveness of six analytical techniques for missing data within the growth curve modeling framework. These techniques include traditional approaches like FIML and TSRE, machine learning approaches by single imputation (K-Nearest Neighbors and missForest), and machine learning approaches by multiple imputation (micecart and miceForest). We investigate the influence of sample size, missing data rate, missing data mechanism, and data distribution on the accuracy and efficiency of model estimation. Our findings indicate that FIML is most effective for MNAR data among the tested approaches. TSRE excels in handling MAR data, while missForest is only advantageous in limited conditions with a combination of very skewed distributions, very large sample sizes (e.g., n larger than 1000), and low missing data rates.
Related papers
- Data-driven Bayesian State Estimation with Compressed Measurement of Model-free Process using Semi-supervised Learning [57.04370580292727]
The research topic is: data-driven Bayesian state estimation with compressed measurement (BSCM) of model-free process.
The dimension of the temporal measurement vector is lower than the dimension of the temporal state vector to be estimated.
Two existing unsupervised learning-based data-driven methods fail to address the BSCM problem for model-free process.
We develop a semi-supervised learning-based DANSE method, referred to as SemiDANSE.
arXiv Detail & Related papers (2024-07-10T05:03:48Z) - On the Performance of Empirical Risk Minimization with Smoothed Data [59.3428024282545]
Empirical Risk Minimization (ERM) is able to achieve sublinear error whenever a class is learnable with iid data.
We show that ERM is able to achieve sublinear error whenever a class is learnable with iid data.
arXiv Detail & Related papers (2024-02-22T21:55:41Z) - Multiple Imputation with Neural Network Gaussian Process for
High-dimensional Incomplete Data [9.50726756006467]
Imputation is arguably the most popular method for handling missing data, though existing methods have a number of limitations.
We propose two NNGP-based MI methods, namely MI-NNGP, that can apply multiple imputations for missing values from a joint (posterior predictive) distribution.
The MI-NNGP methods are shown to significantly outperform existing state-of-the-art methods on synthetic and real datasets.
arXiv Detail & Related papers (2022-11-23T20:54:26Z) - Learning from aggregated data with a maximum entropy model [73.63512438583375]
We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
arXiv Detail & Related papers (2022-10-05T09:17:27Z) - Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions.
In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data.
We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
arXiv Detail & Related papers (2022-01-11T23:01:12Z) - Model-based Clustering with Missing Not At Random Data [0.8777702580252754]
We propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.
Several MNAR models are discussed, for which the cause of the missingness can depend on both the values of the missing variable themselves and on the class membership.
We focus on a specific MNAR model, called MNARz, for which the missingness only depends on the class membership.
arXiv Detail & Related papers (2021-12-20T09:52:12Z) - RIFLE: Imputation and Robust Inference from Low Order Marginals [10.082738539201804]
We develop a statistical inference framework for regression and classification in the presence of missing data without imputation.
Our framework, RIFLE, estimates low-order moments of the underlying data distribution with corresponding confidence intervals to learn a distributionally robust model.
Our experiments demonstrate that RIFLE outperforms other benchmark algorithms when the percentage of missing values is high and/or when the number of data points is relatively small.
arXiv Detail & Related papers (2021-09-01T23:17:30Z) - Imputation-Free Learning from Incomplete Observations [73.15386629370111]
We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation.
We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation.
Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
arXiv Detail & Related papers (2021-07-05T12:44:39Z) - Graph Embedding with Data Uncertainty [113.39838145450007]
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Most subspace learning methods do not take into consideration possible measurement inaccuracies or artifacts that can lead to data with high uncertainty.
arXiv Detail & Related papers (2020-09-01T15:08:23Z) - Missing Data Imputation using Optimal Transport [43.14084843713895]
We leverage optimal transport distances to quantify a criterion and turn it into a loss function to impute missing data values.
We propose practical methods to minimize these losses using end-to-end learning.
These experiments show that OT-based methods match or out-perform state-of-the-art imputation methods, even for high percentages of missing values.
arXiv Detail & Related papers (2020-02-10T15:23:42Z)
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.