This paper discusses two graphical methods for the investigation of local association of two continuous random variables. Often, scalar dependence measures, such as correlation, cannot reflect the complex dependence structure of two variables. However, dependence graphs have the potential to assess a far richer class of bivariate dependence structures. The two graphical methods discussed in this article are the chi-plot and the local dependence map. After the introduction of these methods they are applied to di®erent data sets. These data sets contain simulated data and daily stock return series. With these examples the application possibilities of the two local dependence graphs are shown
Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a p...
The Kendall plot (K-plot) is a plot measuring dependence between the components of a bivariate rando...
In applied psychological, behavioral and sociological research the majority of data are typically mi...
This paper discusses two graphical methods for the investigation of local association of two continu...
There is often more structure in the way two random variables are associated than a single scalar de...
For a bivariate data set the dependence structure can not only be measured globally, for example wit...
Quantifying non-linear dependence structures between two random variables is a challenging task. The...
We propose a new correlation measure for functionally correlated variables based on local linear dep...
In the context of recent interest in extending scalar association measures to local association func...
Title: Study of the dependence structure in economic and financial data Author: Radana Hlavandová De...
In several previous publications we developed an idea how probability tools can be used to measure s...
The final publication is available at link.springer.comWe propose new dependence measures for two re...
We are looking at copula models and dependence measures. Especially the recently developed local dep...
The most common measure of dependence between two time series is the cross-correlation function. Thi...
Different from measures of global dependence, measures of local dependence evaluate the dependence al...
Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a p...
The Kendall plot (K-plot) is a plot measuring dependence between the components of a bivariate rando...
In applied psychological, behavioral and sociological research the majority of data are typically mi...
This paper discusses two graphical methods for the investigation of local association of two continu...
There is often more structure in the way two random variables are associated than a single scalar de...
For a bivariate data set the dependence structure can not only be measured globally, for example wit...
Quantifying non-linear dependence structures between two random variables is a challenging task. The...
We propose a new correlation measure for functionally correlated variables based on local linear dep...
In the context of recent interest in extending scalar association measures to local association func...
Title: Study of the dependence structure in economic and financial data Author: Radana Hlavandová De...
In several previous publications we developed an idea how probability tools can be used to measure s...
The final publication is available at link.springer.comWe propose new dependence measures for two re...
We are looking at copula models and dependence measures. Especially the recently developed local dep...
The most common measure of dependence between two time series is the cross-correlation function. Thi...
Different from measures of global dependence, measures of local dependence evaluate the dependence al...
Composable Markov processes were introduced by Schweder (1970) in order to capture the idea that a p...
The Kendall plot (K-plot) is a plot measuring dependence between the components of a bivariate rando...
In applied psychological, behavioral and sociological research the majority of data are typically mi...