Showing 1 - 10 of 63
Dimension reduction and variable selection play important roles in high dimensional data analysis. The sparse MAVE, a model-free variable selection method, is a nice combination of shrinkage estimation, Lasso, and an effective dimension reduction method, MAVE (minimum average variance...
Persistent link: https://www.econbiz.de/10011056428
Minimum average variance estimation (MAVE, Xia et al. (2002) [29]) is an effective dimension reduction method. It requires no strong probabilistic assumptions on the predictors, and can consistently estimate the central mean subspace. It is applicable to a wide range of models, including time...
Persistent link: https://www.econbiz.de/10009292528
Persistent link: https://www.econbiz.de/10008674176
Persistent link: https://www.econbiz.de/10014545063
Persistent link: https://www.econbiz.de/10014486344
Persistent link: https://www.econbiz.de/10011731307
Persistent link: https://www.econbiz.de/10012191522
When functional data are not homogenous, for example, when there are multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this article, we propose a new estimation procedure for the mixture of Gaussian processes, to incorporate both functional and...
Persistent link: https://www.econbiz.de/10010825840
A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an...
Persistent link: https://www.econbiz.de/10010871393
In this article, we first propose a semiparametric mixture of generalized linear models (GLMs) and a nonparametric mixture of GLMs, and then establish identifiability results under mild conditions.
Persistent link: https://www.econbiz.de/10010906218