27 papers · ranked by Valyu relevance
Γεώργιος Παπαγεωργίου, Benjamin C. Marshall
This article presents an approach to Bayesian semiparametric inference for Gaussian multivariate response regression. We are motivated by various small and medium dimensional problems from the physical and social sciences. The statistical challenges revolve around dealing with the unknown mean and variance functions…
Asokan Mulayath Variyath, Anita Brobbey, Feng Chen
Multivariate multiple regression analysis is often used to assess covariate effects when one or multiple response variables are collected in observational or experimental studies. Many multivariate regression techniques are designed for univariate responses. A common way to deal with multiple response variables is to…
Martin N. Hebart, Chris I. Baker
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the…
Ellen F. Mosleth, Kristian Hovde Liland
Modern analysis technologies output large amounts of multivariate data. The data may come from an experimental design or from other collected observations. We here present a flexible tool, called General Effect Modelling (GEM), for the analysis of any type of multivariate data influenced by one or more qualitative…
Cansu Alakuş, Denis Larocque, Aurélie Labbe
Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the…
Max Goplerud, Michael Auslen
Measuring public opinion at subnational geographies is critical to many theories in political science. Multilevel regression and post-stratification (MRP) is a popular tool for doing so, although existing work is limited to measuring opinion on a single survey question. We provide a framework for estimating the joint…
Yuanqing Lu, Timur Fazletdinov, Zhiwen Pan, Katrin Wondraczek + 1 more
The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade-off relations, for example, in terms of product composition, yield, or energy efficiency. Machine-supported process evaluation and learning (ML) of these relations enables optimization…
Patrick J. Miller, Gitta H. Lubke, Daniel B. McArtor, C. S. Bergeman
This research was based upon work supported by the National Science Foundation Graduate Research Fellowship Program under grant number 1313583. The second author is supported by NIDA R37 DA-018673. The fourth author is supported by a grant from the National Institute of Aging (1 R01 AG023571-A1-01). The computational…
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…
Anita Brobbey, Samuel Wiebe, Alberto Nettel-Aguirre, Colin Bruce Josephson + 3 more
generalized estimation equations Authors: ['Anita Brobbey' 'Samuel Wiebe' 'Alberto Nettel-Aguirre' 'Colin Bruce Josephson' 'Tyler Williamson' 'Lisa M Lix' 'Tolulope T. Sajobi'] Discriminant analysis procedures that assume parsimonious covariance and/or means structures have been proposed for distinguishing between two…
Franz Classe, Christoph Kern
Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential…
Arnaud Le Rouzic, Clémentine Renneville, Alexis Millot, Simon Agostini + 2 more
Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest…
Joakim Nyberg, E. Niclas Jonsson, Mats O. Karlsson, Jonas Häggström
Two full model approaches was compared with respect to their ability to handle missing covariate information. The reference data analysis approach was the full model method in which the covariate effects are estimated conventionally using fixed effects, and missing covariate data is imputed with the median of the…
Carl F. Falk, Unhee Ju
Recent years have seen a dramatic increase in item response models for measuring response styles on Likert-type items. These model-based approaches stand in contrast to traditional sum-score-based methods where researchers count the number of times that participants selected certain response options. The…
Deniz Akdemir
Missing data present an important challenge when dealing with high dimensional data arranged in the form of an array. In this paper, we propose methods for estimation of the parameters of array variate normal probability model from partially observed multi-way data. The methods developed here are useful for missing…
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
Pieter C. Schoonees, Michel van de Velden, Patrick J. F. Groenen
Dual scaling (DS) is a multivariate exploratory method equivalent to correspondence analysis when analysing contingency tables. However, for the analysis of rating data, different proposals appear in the DS and correspondence analysis literature. It is shown here that a peculiarity of the DS method can be exploited to…
Baptiste Goujaud, Eric W. Tramel, Pierre Courtiol, Mikhail Zaslavskiy + 1 more
'Mikhail Zaslavskiy' 'Gilles Wainrib'] Detection of interactions between treatment effects and patient descriptors in clinical trials is critical for optimizing the drug development process. The increasing volume of data accumulated in clinical trials provides a unique opportunity to discover new biomarkers and further…
Authors not listed
Presence of gold nanoparticles in an aqueous dispersion perturbs water molecules in their vicinity. Such water molecules form what is known as hydration shell and possess different vibrational attributes than those in the bulk dispersion. Raman spectroscopy was utilised to study these hydration shell water molecules…
Martijn Schoenmakers, Maria Bolsinova, Jesper Tijmstra
Extreme and midpoint response styles have frequently been found to decrease the validity of Likert-type questionnaire results. Different approaches for modelling extreme and midpoint responding have been proposed in the literature, with some advocating for a unidimensional conceptualization of the response styles as…
Jiwei Zhang, Jing Lu, Feng Chen, Jian Tao
In many large-scale tests, it is very common that students are nested within classes or schools and that the test designers try to measure their multidimensional latent traits (e.g., logical reasoning ability and computational ability in the mathematics test). It is particularly important to explore the influences of…
Authors not listed
Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
Martijn Schoenmakers, Jesper Tijmstra, Jeroen Vermunt, Maria Bolsinova
'Maria Bolsinova'] Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model…
Holger Schielzeth, Shinichi Nakagawa
Individuals differ in average phenotypes and in sensitivity to environmental variation. Such context-sensitivity can be modelled as random-slope variation. Random-slope variation implies that the proportion of between-individual variation varies across the range of a covariate (environment/context/time/age) and has…
Authors not listed
Processing high dimensional and complex monoclonal antibody (mAb) bioprocess data in industry is now more efficient due to conversational AI. The human in the loop approach to Large Language Model (LLM) inferencing with document retrieval and chained outputs is a probable benefit to existing biotechnology workflows.…
Yichi Zhang, Siyu Tao, Wei Chen, Daniel W. Apley
Computer simulations often involve both qualitative and numerical inputs. Existing Gaussian process (GP) methods for handling this mainly assume a different response surface for each combination of levels of the qualitative factors and relate them via a multiresponse cross-covariance matrix. We introduce a…
Aniket Chitre, David Woods, Alexei Lapkin
Liquid formulations design typically involves searching a high-dimensional space, owing to the combinatorial selection of ingredients from a larger subset of available ingredients, with a relatively limited experimental budget. Therefore, we need to efficiently select the most informative experiments. These experiments…