Sparse Linear Identifiable Multivariate Modeling [PDF]
, 2011 In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data.
Aapo Hyvärinen +4 more
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`Been there done that': Disentangling option value effects from user heterogeneity when valuing natural resources with a use component. [PDF]
, 2009 Endogeneity bias arises in contingent valuation studies when the error term in the willingness to pay (WTP) equation is correlated with explanatory variables because observable and unobservable characteristics of the respondents affect both their WTP ...
Lyssenko, Nikita +1 more
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Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2020 Uganda seeks to transform its society from a peasant to a modern and largely urban society by the year 2040. To achieve this, electricity as a form of modern and clean energy has been identified as a driving force for all the sectors of the economy.
Kürşat Ayan, Abdal Kasule
doaj +1 more source
MissForest - nonparametric missing value imputation for mixed-type data
, 2011 Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. J. Stekhoven +11 more
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USING TRAJECTORIES FROM A BIVARIATEGROWTH CURVE OF COVARIATES IN A COXMODEL ANALYSIS [PDF]
, 2004 In many maintenance treatment trials, patients are first enrolled into an open treatmentbefore they are randomized into treatment groups. During this period, patients are followedover time with their responses measured longitudinally. This design is very
Dang, Qianyu
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Matrix-Variate Regressions and Envelope Models
, 2017 Modern technology often generates data with complex structures in which both response and explanatory variables are matrix-valued. Existing methods in the literature are able to tackle matrix-valued predictors but are rather limited for matrix-valued ...
Cook, R. Dennis, Ding, Shanshan
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Dealing with the Outlier Problem in Multivariate Linear Regression Analysis Using the Hampel Filter
Kurdistan Journal of Applied ResearchOutliers in multivariate linear regression models can significantly distort parameter estimates, leading to biased results and reduced predictive accuracy.
Amira Wali Omer, Taha Hussein Ali
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Chlorophyll-A Time Series Study on a Saline Mediterranean Lagoon: The Mar Menor Case
Engineering ProceedingsThe Mar Menor, Europe’s largest saline lagoon, has experienced significant eutrophication. The concentration of chlorophyll-a (Chl-a) in the water is used as a critical indicator of this eutrophication process and can alert us to possible ecosystemic ...
Arnau Garcá-i-Cucó +4 more
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Fusion of data sets in multivariate linear regression with errors-in-variables [PDF]
We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors--in--variables, in the case where various data sets are merged into a single analysis and the observable ...
Albert Satorra
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Anthropometric multicompartmental model to predict body composition In Brazilian girls
BMC Sports Science, Medicine and Rehabilitation, 2017 Background Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt,
Dalmo Machado +5 more
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