multivariate quantile regressionmultiresponse linear modelsequivarianceloss functionasymptotic normalityblood pressuressales performancesTo detect the dependence on the covariates in the lower and upper tails of the response distribution, regression quantiles are very useful tools in linear model problems with ...
A scoring rule S(G,y) is a function of any forecast G (e.g. a point forecast, a quantile forecast or a PDF) and an observation y. The scoring evaluates to a summary measure, which we denote the score. Given a time series {yt}={y1,⋯,yN}, every pair of forecast and correspon...
You can also visualize multivariate data by using glyphs to represent the dimensions. The functionglyphplotsupports two types of glyphs: stars and Chernoff faces. Create a star plot of the first nine car models in the data set. Each spoke in a star represents one variable, and the spoke leng...
Dynamic quantile models In a time series context, quantile regression approaches need to be adapted to account for the dependence induced by the time-ordering of the data. A natural extension is the quantile autoregressive approach developed by Koenker and Xiao (Citation2006) and, as pointed out ...
influence functionmultivariate responsesnonparametric maximum likelihoodrobust regressionWe propose a M-quantile regression model for the analysis of multivariate, continuous, longitudinal data. M-quantile regression represents an appealing alternative to standard regression models, as it combines the robustness ...
Introducing mvgam for fitting Dynamic Generalized Additive Models We can explore the package’s primary functions using a dataset that is available with all R installations. Load the lynx data and plot the series as well as its autocorrelation function data(lynx) lynx_full <- data.frame(year =...
Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approa
This package provides methods to estimate models of the formwhere is simple distribution, we observe periods, and , , , and are known. What is multivariate is (though, can also be multivariate) and this package is written to scale well in the cardinality of . The package uses independent ...
the superlevel set is a function of conditioning variables much like in quantile regression. We show that conditional superlevel sets have favorable mathematical and intuitive features, and support a clear probabilistic interpretation. We derive the superlevel sets for a conditional or marginal density...
10 % quantile 1 0.98 1.02 1 0.99 90 % quantile 1.01 1.02 1 1 0.99 99 % quantile 1 1 1 0.99 0.99 6. Conclusion In this paper, we investigate the cross-sectional extreme risk spillovers from the US stock market to BRICS markets by using the MVMQ models framework. We further investigate...