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Regression-based latent factor models

Deepak Agarwal, Bee-Chung Chen
2009 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09  
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]

Silvia Montagna, Tor Wager, Lisa Feldman-Barrett, Timothy D. Johnson,, Thomas E. Nichols
2016 arXiv   pre-print
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

Silvia Montagna, Tor Wager, Lisa Feldman Barrett, Timothy D. Johnson, Thomas E. Nichols
2017 Biometrics  
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]

Ajitabh Kumar
2022 arXiv   pre-print
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

Akbar Hassanzadeh, Zahra Heidari, Awat Feizi, Ammar Hassanzadeh Keshteli, Hamidreza Roohafza, Hamid Afshar, Payman Adibi
2017 Computational and Mathematical Methods in Medicine  
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

Stephanie T. Lanza
2011 Open Family Studies Journal  
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

Rezzy Eko Caraka, Rung Ching Chen, Youngjo Lee, Prana Ugiana Gio, Arif Budiarto, Bens Pardamean
2021 Procedia Computer Science  
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

Yunxia Liu, Shiwen Jiang, Yanxun Liu, Rui Wang, Xiao Li, Zhongshang Yuan, Lixia Wang, Fuzhong Xue
2011 International Journal of Health Geographics  
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

H Li, F Hong
2001 Genome Biology  
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

Li Cai
2012 Shanghai Archives of Psychiatry  
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

Wenyi Sun, Jianhua Gong, Jieping Zhou, Yanlin Zhao, Junxiang Tan, Abdoul Ibrahim, Yang Zhou
2015 International Journal of Environmental Research and Public Health  
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

Paul Lodder, Wilco H.M. Emons, Johan Denollet, Jelte M. Wicherts
2020 Structural Equation Modeling  
, (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

Matheus Pereira Liborio, Petr Ekel, Renata de Mello Lyrio, Patricia Bernardes, Gustavo Luis Soares, Thiago Melo Machado-Coelho
2020 IEEE Access  
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

Ned Kock
2015 Information Management and Business Review  
–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

Behzad Bamdad Mehrabani, Babak Mirbaha
2018 Civil Engineering Journal  
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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