Spatially-adaptive sensing in nonparametric regression
نویسندگان
چکیده
منابع مشابه
Bayesian mixture of splines for spatially adaptive nonparametric regression
A Bayesian approach is presented for spatially adaptive nonparametric regression where the regression function is modelled as a mixture of splines. Each component spline in the mixture has associated with it a smoothing parameter which is defined over a local region of the covariate space. These local regions overlap such that individual data points may lie simultaneously in multiple regions. C...
متن کاملBayesian adaptive nonparametric M-regression
Nonparametric regression has been popularly used in curve fitting, signal denosing, and image processing. In such applications, the underlying functions (or signals) may vary irregularly, and it is very common that data are contaminated with outliers. Adaptive and robust techniques are needed to extract clean and accurate information. In this paper, we develop adaptive nonparametric M-regressio...
متن کاملAdaptive lifting for nonparametric regression
Many wavelet shrinkage methods assume that the data are observed on an equally spaced grid of length of the form 2J for some J . These methods require serious modification or preprocessed data to cope with irregularly spaced data. The lifting scheme is a recent mathematical innovation that obtains a multiscale analysis for irregularly spaced data. A key lifting component is the “predict” step w...
متن کاملSpatially Adaptive Bayesian Regression Splines
In this paper we study penalized regression splines (P-splines), which are low–order basis function splines with a penalty to avoid undersmoothing. Such P–splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. While frequentist methods are available to address this issue, no Bayesian techniques have been developed. Our approach is to model 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: 2013
ISSN: 0090-5364
DOI: 10.1214/12-aos1064