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extension of an existing work on reducing dimensionality for model-based clustering based on Gaussian mixtures. Information on …
Persistent link: https://www.econbiz.de/10010794021
The importance of dimension reduction has been increasing according to the growth of the size of available data in many fields. An appropriate dimension reduction method of raw data helps to reduce computational time and to expose the intrinsic structure of complex data. Sliced inverse...
Persistent link: https://www.econbiz.de/10010871383
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional covariate x is considered. A new approach is proposed based on sliced inverse regression (SIR) for estimating the effective dimension reduction (EDR) space without requiring a prespecified parametric...
Persistent link: https://www.econbiz.de/10010871397
Research on social capital in general and trust in particular is markedly increased. The worldwide problem of low and decreasing levels of trust in many public institutions is greatly debated. This is a very important problem because the trust citizens have in public institutions may foster...
Persistent link: https://www.econbiz.de/10010843861
Persistent link: https://www.econbiz.de/10010845790
In recent years, evidence has emerged indicating that magnetic resonance imaging (MRI) brain scans provide valuable diagnostic information about Alzheimer’s disease. It has been shown that MRI brain scans are capable of both diagnosing Alzheimer’s disease itself at an early stage and...
Persistent link: https://www.econbiz.de/10010846122
Persistent link: https://www.econbiz.de/10010847677
Modelling covariance structures is known to suffer from the curse of dimensionality. In order to avoid this problem for forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures that accommodates asymmetry and long memory....
Persistent link: https://www.econbiz.de/10010907411
We propose multivariate classification as a statistical tool to describe business cycles. These cycles are often analyzed as a univariate phenomenon in terms of GNP or industrial net production ignoring additional information in other economic variables. Multivariate classification overcomes...
Persistent link: https://www.econbiz.de/10010982396
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy tailed distributions. We show that the recently proposed MAVE and OPG methods by Xia et al. (2002) allow us to make them robust in a relatively straightforward way...
Persistent link: https://www.econbiz.de/10010983843