Journal of Data Science

Missing data is a common occurrence in various fields, spanning social science, education, economics, and biomedical research. Disregarding missing data in statistical analyses can introduce bias to study outcomes. To mitigate this issue, imputation methods have proven effective in reducing nonresponse bias and generating complete datasets for subsequent analysis of secondary data. The efficacy of imputation methods hinges on the assumptions of the underlying imputation model. While machine learning techniques such as regression trees, random forest, XGBoost, and deep learning have demonstrated robustness against model misspecification, their optimal performance may necessitate fine-tuning under specific conditions. Moreover, imputed values generated by these methods can sometimes deviate unnaturally, falling outside the normal range. To address these challenges, we propose a novel Predictive Mean Matching imputation (PMM) procedure that leverages popular machine learning-based methods. PMM strikes a balance between robustness and the generation of appropriate imputed values. In this paper, we present our innovative PMM approach and conduct a comparative performance analysis through Monte Carlo simulation studies, assessing its effectiveness against other established methods.

Abstract: Cancer is a complex disease where various types of molecular aber rations drive the development and progression of malignancies. Among the diverse molecular aberrations, inherited and somatic mutations on DNA se quences are considered as major drivers for oncogenesis. The complexity of somatic alterations is revealed from large-scale investigations of cancer genomes and robust methods for interring the function of genes. In this review, we will describe sequence mutations of several cancer-related genes and discuss their functional implications in cancer. In addition, we will in troduce the on-line resources for accessing and analyzing sequence mutations in cancer. We will also provide an overview of the statistical and computa tional approaches and future prospects to conduct comprehensive analyses of the somatic alterations in cancer genomes.

Abstract: Panel data transcends cross-sectional data by tapping pooled inter- and intra-individual differences, along with between and within individual variation separately. In the present study these micro variations in ill-being are predicted by psychological indicators constructed from the British Household Panel Survey (BHPS). Panel regression effects are corrected for errors-in-variables, which attenuate slopes estimated by traditional panel regressions. These corrections reveal that unhappiness and life dissatisfaction are distinct variables that have different psychological causations.

Abstract: Latent class analysis (LCA) is a popular method for analyzing multiple categorical outcomes. Given the potential for LCA model assump tions to influence inference, model diagnostics are a particulary important part of LCA. We suggest using the rate of missing information as an addi tional diagnostic tool. The rate of missing information gives an indication of the amount of information missing as a result of observing multiple sur rogates in place of the underlying latent variable of interest and provides a measure of how confident one can be in the model results. Simulation studies and real data examples are presented to explore the usefulness of the proposed measure.

Clustering is an essential technique for discovering patterns in data. Many clustering algorithms have been developed to tackle the ever increasing quantity and complexity of data, yet algorithms that can cluster data with mixed variables (continuous and categorical) remain limited despite the abundance of mixed-type data. Of the existing clustering methods for mixed data types, some posit unverifiable distributional assumptions or rest on unbalanced contributions of different variable types. To address these issues, we propose a two-step hybrid density- and partition-based (HyDaP) algorithm to detect clusters after variable selection. The first step involves both density-based and partition-based algorithms to identify the data structure formed by continuous variables and determine important variables (both continuous and categorical) for clustering. The second step involves a partition-based algorithm together with our proposed novel dissimilarity measure to obtain clustering results. Simulations across various scenarios were conducted to compare the HyDaP algorithm with other commonly used methods. Our HyDaP algorithm was applied to identify sepsis phenotypes and yielded important results.