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Regression-based latent factor models
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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Our approach is based on a model that predicts response as a multiplicative function of row and column latent factors that are estimated through separate regressions on known row and column features. ...
We propose a novel latent factor model to accurately predict response for large scale dyadic data in the presence of features. ...
Given features X new ij
Table 1 : 1 Regression-based Latent Factor Model sponse when both observed features and latent factors are known. ...
doi:10.1145/1557019.1557029
dblp:conf/kdd/AgarwalC09
fatcat:b6lfktz2gjbe3kunogbkimmrxu
Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data
[article]
2016
arXiv
pre-print
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A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. ...
As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-based Meta-analysis (CBMA). ...
model on the latent factors. ...
arXiv:1606.06912v1
fatcat:dzwq2mdqmbe3hkmh7b3p3zvn5i
Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data
2017
Biometrics
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A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. ...
As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). ...
Spatial Bayesian latent factor regression for CBMA In this Section, we present our spatial Bayesian latent factor regression for CBMA data. ...
doi:10.1111/biom.12713
pmid:28498564
pmcid:PMC5682245
fatcat:fsyuy5635ve7llvr4c4oc4jh7i
Surrogate Model For Field Optimization Using Beta-VAE Based Regression
[article]
2022
arXiv
pre-print
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Surrogate model developed using beta-VAE based regression finds interpretable and relevant latent representation. ...
latent space, which is then further used for regression. ...
Olympus benchmark reservoir model was made available by the Netherlands Organization for Applied Scientific Research (TNO). ...
arXiv:2008.11433v2
fatcat:dygvtsmnx5futdgqbno6hxbovy
Association of Stressful Life Events with Psychological Problems: A Large-Scale Community-Based Study Using Grouped Outcomes Latent Factor Regression with Latent Predictors
2017
Computational and Mathematical Methods in Medicine
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Grouped outcomes latent factor regression on latent predictors was used for modeling the association of psychological problems (depression, anxiety, and psychological distress), measured by Hospital Anxiety ...
In addition, in the adjusted models, the regression coefficients for the association of social stressors domain and psychological problems profile score were 0.37, 0.35, and 0.46 in total sample, males ...
females, respectively ( < 0.001), based on continuous latent factor model. ...
doi:10.1155/2017/3457103
pmid:29312459
pmcid:PMC5625761
fatcat:l3en374jfffv5mnrzmu2czsvqq
Differential Effects for Sexual Risk Behavior: An Application of Finite Mixture Regression
2011
Open Family Studies Journal
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Regression-based methods are commonly used to estimate the average effects of risk factors, however such results can be difficult to translate to prevention implications at the individual level. ...
Four latent classes were identified on the basis of the Poisson regression parameter estimates. Gender, race, and grade were included as predictors of latent class membership. ...
regression coefficients based on the finite mixture regression model. ...
doi:10.2174/1874922401104010081
pmid:23125880
pmcid:PMC3487167
fatcat:uosd6payjrbbxcohilp2i3nljy
Latent Regression and Ordination Risk of Infectious Disease and Climate
2021
Procedia Computer Science
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The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. ...
The purpose of this paper is to examine more detail and comprehensively about the relationship among climate and event disease count in Taiwan using the partial least square latent regression model. ...
Brief Overview of Latent Regression The structural model with partial least square latent regression is designed for the recursive model, which is a model that describes the causal relationship between ...
doi:10.1016/j.procs.2020.12.004
fatcat:3thzoxiwjzginpnvrg6ny7ozwu
Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosis
2011
International Journal of Health Geographics
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regression model between the latent synthetic DR-TB factor ("DR-TB") and latent synthetic risk factors. ...
We should formulate regional DR-TB monitoring planning and prevention and control strategies, based on the spatial characteristics of the latent synthetic risk factors and spatial variability of the local ...
We are also pleased to acknowledge the World Climate website, the Health resource database of the WHO website and other data sources for providing us with ecological risk factors data. ...
doi:10.1186/1476-072x-10-50
pmid:21812998
pmcid:PMC3173290
fatcat:leikpbp4hbc23mem37fogb7zue
Cluster-Rasch models for microarray gene expression data
2001
Genome Biology
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The cluster-Rasch model provides a probabilistic model for describing gene expression patterns across samples and can be used to relate gene expression profiles to phenotypes. ...
We propose two different formulations of the Rasch statistical models to the problem of relating gene expression profiles to the phenotypes. ...
One advantage of the proposed method is that we can model the interactions between the latent factors in the standard way of modeling interactions in the regression models. ...
pmid:11532215
pmcid:PMC55328
fatcat:5noyolobzbar7amwigeeh3wiku
Latent variable modeling
2012
Shanghai Archives of Psychiatry
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The regression coefficient λ i is called the factor loading of variable y i on common factor ξ, representing the strength of association between the observed variable and the latent common factor. ...
There exists another equivalent way of representing the factor analysis model, using a path diagram. Figure 1 is largely based on Jöreskog's [3] example for sets of congeneric tests. ...
doi:10.3969/j.issn.1002-0829.2012.02.010
pmid:25324615
pmcid:PMC4198841
fatcat:gdznowjydzdgxif4bykjgo4rli
A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology
2015
International Journal of Environmental Research and Public Health
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A geographically weighted regression (GWR) model was used to explore the local association between factors and TB prevalence. ...
EFA and PLS-PM indicated significant associations between TB prevalence and its latent factors. ...
Analysis Using a Geographical Statistical Model Based on the latent variable scores from the PLS-PM, the geographically weighted regression (GWR) model was implemented to explore the local spatial heterogeneity ...
doi:10.3390/ijerph120201425
pmid:25633032
pmcid:PMC4344675
fatcat:7ptnbflidzfrhkyp4d4fyl4vbq
Latent logistic interaction modeling: A simulation and empirical illustration of Type D personality
2020
Structural Equation Modeling
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, (2) factor scores, and (3) Structural Equation Modeling (SEM). ...
This study focuses on three popular methods to model interactions between two constructs containing measurement error in predicting an observed binary outcome: logistic regression using (1) observed scores ...
Factor score logistic regression differs from latent variable modeling approach in that it first estimates for each person the factor scores based on the item scores (Step 1), and then uses those estimated ...
doi:10.1080/10705511.2020.1838905
fatcat:otykjiqzgfc4vi5i5jnygbujx4
Structural equation modeling applied to Internet consumption forecast in Brazil
2020
IEEE Access
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The factor analysis combined with the partial least squares regression was performed by the PLS-SEM. ...
The first step in applying the PLS-SEM Algorithm concerns to the factor analysis. The observed variables that influences the latent variable measured through the factor loading remain in the model. ...
doi:10.1109/access.2020.3016286
fatcat:44un3sqlwfcejcg42jrvj5loba
PLS-based SEM Algorithms: The Good Neighbor Assumption, Collinearity, and Nonlinearity
2015
Information Management and Business Review
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–PLS regression, PLS Mode A, and PLS Mode B. ...
With the goal of improving that understanding, we provide a discussion on the treatment of reflective and formative latent variables in the context of three main algorithms used in PLS-based SEM analyses ...
With the PLS regression algorithm, a confirmatory factor analysis may be conducted without an inner model; i.e., with a "model" without links among latent variables. ...
doi:10.22610/imbr.v7i2.1146
fatcat:o7yoq4brrbfljcmsdfkeayedvm
Evaluating the Relationship between Operating Speed and Collision Frequency of Rural Multilane Highways Based on Geometric and Roadside Features
2018
Civil Engineering Journal
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Structural equation modelling (SEM) with latent variables was employed to model operating speed and collision frequency, simultaneously. ...
The results show that the latent variable "roadside effect" increases collision frequency by a standard regression weight of 3.455; however, it reduces operating speed by a standard regression weight of ...
There are several studies for collision frequency modeling based on the Poisson model [5, 9, [12] [13] [14] and negative binomial regression model [4, [14] [15] [16] . ...
doi:10.28991/cej-0309120
fatcat:ayggxhmxkvf6jdnmdbfhuvf6bm
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