Univariate and multivariate associations with death
MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics
... Background: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has ...over ...
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Bias in Estimating Multivariate and Univariate Diffusions
... Abstract Multivariate continuous time models are now widely used in economics and ...linear multivariate continuous time systems that includes the commonly used Euler and trapezoidal approximations as ...
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Univariate and Multivariate Time Series Manifold Learning
... Time series analysis aims to extract meaningful information from data that has been generated in sequence by a dynamic process. The modelling of the non-linear dynamics of a signal is often performed using a linear space ...
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A Univariate, Bivariate and Multivariate Extensions for the Inverse Rayleigh Model with Properties and Applications to the Univariate Version
... A new univariate extension of the Inverse Rayleigh distribution is proposed and studied. Some of its fundamental statistical properties such as some stochastic properties, ordinary and incomplete moments, moments ...
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Shapelet Transforms for Univariate and Multivariate Time Series Classification
... design multivariate shapelet algorithms it became apparent that more drastic time and space reductions would be required as we were still struggling on univariate ...
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On the Univariate Representation of Multivariate Volatility Models with Common Factors
... some multivariate models accounting for co-movements and show that the implied marginal volatility processes are of low ...general multivariate framework, with correlated conditional variances and contagion ...
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Essays on univariate and multivariate modeling of financial market risks
... (yielding multivariate models of even higher flexibility), we construct C- and D-vines with mixture pair-copulas to minimize the possibility of misspecifying a vine ...
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The effect of univariate bias adjustment on multivariate hazard estimates
... Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between ...
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Assessing Univariate and Multivariate Normality, A Guide For Non-Statisticians
... both univariate and multivariate normality, several methods have been ...the univariate setting, the Q-Q plot, histogram, box plot, stem-and-leaf plot or dot plot are some graphical methods that can ...
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The effect of univariate bias adjustment on multivariate hazard estimates
... Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between ...
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Multivariate models for pretest posttest data and a comparison to univariate models
... study multivariate models are investigated for pretest posttest data and a comparison is made with univariate methods, the change score method and the regressor variable ...
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Comparison of univariate and multivariate linkage analysis of traits related to hypertension
... a univariate and multivariate linkage approach to analyze SBP or traits related to HTN (and highly correlated with SBP) - TG, LDL, and HDL, ...the univariate results ...
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THE USE OF PRINCIPAL COMPONENTS AND UNIVARIATE CHARTS TO CONTROL MULTIVARIATE PROCESSES
... This paper is organized as follows: the next section presents a revision of different types of control charts for monitoring multivariate processes. In Section 3, we first investigate the use of principal ...
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Comparing univariate and multivariate models to forecast portfolio value-at-risk
... whether multivariate or univariate models are most appropriate for the problem of portfolio VaR ...that multivariate GARCH models outperformed competing univariate models on an out-of-sample ...
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Temporal aggregation of univariate and multivariate time series models: A survey
... 7 Conclusion We provide a comprehensive and up-to-date survey of temporal aggregation for univariate and multivari- ate mean and variance time series models, which has so far been lacking in the literature. We ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... The multivariate posterior distribution f (Λ|ω, R) is high dimensional and has no closed-form ...T univariate conditional posteriors for λ t |Λ \t , ω, R, where Λ \t denotes Λ without the tth ...the ...
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Classical and Bayesian Analysis of Univariate and Multivariate Stochastic Volatility Models
... The multivariate posterior distribution f (Λ|ω, R) is high dimensional and has no closed-form ...T univariate conditional posteriors for λ t |Λ \t , ω, R, where Λ \t denotes Λ without the tth ...the ...
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Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study
... using univariate and multivariate pattern ...the univariate model of the amplitude of low-frequency BOLD fluctuations (ALFF) and gray matter volume (GMV) making use of resting-state fMRI and ...
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Univariate versus Multivariate Models for Short-term Electricity Load Forecasting
... the univariate and multivariate models, we use a nonparametric test to see if the differences between their post-sample accuracies are statistically significant (that is, if we can say, with some ...
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Fuzzy clustering of univariate and multivariate time series by genetic multiobjective optimization
... Abstract: Given a set of time series, it is of interest to discover subsets that share similar properties. For instance, this may be useful for identifying and estimating a single model that may fit conveniently several ...
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