26 papers · ranked by Valyu relevance
Rick Farouni
In general, we will be limiting our focus mainly to the the multivariate statistics setting in which we treat an observation as a multivariate random vector yn = (yn,1, . . . , yn,P ) consisting of P features and the data as a set of N observations y = {y1, · · · , yn, · · · , yN }. Accordingly, we can think of the…
Jan-Willem Romeijn, Jon Williamson
We consider the use of interventions for resolving a problem of unidentified statistical models. The leading examples are from latent variable modelling, an influential statistical tool in the social sciences. We first explain the problem of statistical identifiability and contrast it with the identifiability of causal…
Andriy Serdega, Dae‐Shik Kim
1 Declaration of Ethical Conduct in Research: I, as a graduate student of Korea Advanced Institute of Science and Technology, hereby declare that I have not committed any act that may damage the credibility of my research. This includes, but is not limited to, falsification, thesis written by someone else, distortion…
Christopher J. Schmank, Sara Anne Goring, Kristof Kovacs, Andrew R. A. Conway
'Andrew R. A. Conway'] The positive manifold-the finding that cognitive ability measures demonstrate positive correlations with one another-has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective…
Nicola Melillo, Adam S. Darwich
In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has…
Matthew R. Whiteway, Daniel A. Butts
The activity of sensory cortical neurons is not only driven by external stimuli, but is also shaped by other sources of input to the cortex. Unlike external stimuli these other sources of input are challenging to experimentally control or even observe, and as a result contribute to variability of neuronal responses to…
Klaus K. Holst, Esben Budtz‐Jørgensen
An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features are implemented including…
Ji Hoon Ryoo, Cixin Wang, Susan M. Swearer, Michael Hull + 1 more
In the areas of social and behavioral sciences, factor analysis has been a long-standing analytic strategy to understand unobserved (or latent) constructs as well as their internal structure from observed data (Cudeck and MacCallum, ). When the unobserved constructs are introduced using factor analysis, researchers are…
Bert van der Veen, Francis K.C. Hui, Knut A. Hovstad, Robert B. O’Hara
In community ecology, unconstrained ordination can be used to predict latent variables from a multivariate dataset, which generated the observed species composition. Latent variables can be understood as ecological gradients, which are represented as a function of measured predictors in constrained ordination, so that…
Isabella Gollini, Thomas Brendan Murphy
Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data…
Muhammad Ammar Malik, Tom Michoel
Random effect models are popular statistical models for detecting and correcting spurious sample correlations due to hidden confounders in genome-wide gene expression data. In applications where some confounding factors are known, estimating simultaneously the contribution of known and latent variance components in…
Pär Jonsson, Benny Björkblom, Elin Chorell, Tommy Olsson + 1 more
Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a…
Augustin Kelava, Holger Brandt
In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, [41]), and multilevel modeling (Rabe-Hesketh…
Dimitrios Stamovlasis, George Papageorgiou, Georgios Tsitsipis, Themistoklis Tsikalas + 1 more
This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes…
Francesco Bartolucci, Giorgio E. Montanari, Silvia Pandolfi
We propose a modified version of the three-step estimation method for the latent class model with covariates, which may be used to estimate latent Markov models for longitudinal data. The three-step estimation approach we propose is based on a preliminary clustering of sample units on the basis of the time specific…
Zsuzsa Bakk, Jouni Kuha
In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund-Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an…
Ruofan Wang, Lei Fang, Yue Wang, Jin Jin
Leveraging observational data to understand the associations between risk factors and disease outcomes and conduct disease risk prediction is a common task in epidemiology. While traditional linear regression and other machine learning models have been extensively implemented for this task, the associations between…
Ziang Zhang, Jerald F. Lawless, Andrew D. Paterson, Lei Sun
In genome-wide association studies (GWAS), it is desirable to test for interactions (GxE) between single-nucleotide polymorphisms (SNPs,G’s) and environmental variables (E’s). However, directly accounting for interaction is often infeasible, because E is latent. For quantitative traits (Y) that are approximately…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Francesco Bartolucci, Alessio Farcomeni, Silvia Pandolfi, Fulvia Pennoni
'Fulvia Pennoni'] Latent Markov (LM) models represent an important class of models for the analysis of longitudinal data (Bartolucci et al., 2013), especially when response variables are categorical. These models have a great potential of application for the analysis of social, medical, and behavioral data as well as…
Authors not listed
Computational methods for generating molecules with specific physiochemical properties or biolog- ical activity can greatly assist drug discovery efforts. Deep learning generative models constitute a significant step towards that direction. In this work, we introduce a novel approach that utilizes a Reinforcement…
Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha
We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an…
Basile Jumentier, Kevin Caye, Barbara Heude, Johanna Lepeule + 1 more
Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to remove variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor…
Victor H. R. Nogueira, Rishabh Sharma, Rafael V. C. Guido, Michael J. Keiser
As efforts to improve the robustness of molecular representations advance, so does the need for methods to test and validate them. We use a Variational Auto-Encoder (VAE), an unsupervised deep learning model, to generate anomalous samples of a well-known molecular string format called SELF-referencIng Embedded Strings…
Samuel Renaud, Rachael Mansbach
Current antibacterial treatments cannot overcome the rapidly growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…