High-dimensional additive modeling
نویسندگان
چکیده
منابع مشابه
High-Dimensional Additive Modeling
We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well as performance for finite-sample data. We present a computationally efficient algorithm, with provable numerical convergence properties, for optimizing the penalized likelihood. Furthermore, we provide oracle results...
متن کاملHigh-Dimensional Sparse Additive Hazards Regression
High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and credit risk analysis. In this article, we propose a class of regularization methods for simultaneous variable selection and estimation in the additive hazards model, by combining the nonconcave penalized likelihood appr...
متن کاملSparse Regularization for High Dimensional Additive Models
We study the behavior of the l1 type of regularization for high dimensional additive models. Our results suggest remarkable similarities and differences between linear regression and additive models in high dimensional settings. In particular, our analysis indicates that, unlike in linear regression, l1 regularization does not yield optimal estimation for additive models of high dimensionality....
متن کاملModeling High Dimensional Time Series
This paper investigates the effectiveness of the recently proposed Gaussian Process Dynamical Model (GPDM) on high dimensional chaotic time series. The GPDM takes a Bayesian approach to modeling high-dimensional time series data, using the Gaussian process Latent Variable model (GPLVM) for nonlinear dimensionality reduction combined with a nonlinear dynamical model in latent space. The GPDM is ...
متن کاملin High-Dimensional Numerical Modeling
In the present paper, we discuss the novel concept of super-compressed tensor-structured data formats in high dimensional applications. We describe the multi-folding or quantics based tensor approximation method of O(d logN)-complexity (logarithmic scaling in the volume size), applied to the discrete functions over the product index set {1, ..., N}⊗d, or briefly N -d tensors of size N, and to t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2009
ISSN: 0090-5364
DOI: 10.1214/09-aos692