Smoothing Spline Density Estimation: Theory
نویسندگان
چکیده
منابع مشابه
Smoothing Spline Density Estimation : Conditional Distribution
This article extends recent developments in penalized likelihood probability density estimation to the estimation of conditional densities on generic domains. Positivity and unity constraints for a probability density are enforced through a one-to-one logistic conditional density transform made possible by term trimming in an ANOVA decomposition of multivariate functions. The construction of mo...
متن کاملSmoothing Spline Estimation of Variance Functions
This article considers spline smoothing of variance functions. We focus on selection of smoothing parameters and develop three direct data-driven methods: unbiased risk (UBR), generalized approximate cross validation (GACV) and generalized maximum likelihood (GML). In addition to guaranteed convergence, simulations show that these direct methods perform better than existing indirect UBR, genera...
متن کاملUniversal smoothing factor selection in density estimation: theory and practice
In earlier work with Gabor Lugosi, we introduced a method to select a smoothing factor for kernel density estimation such that, for all densities in all dimensions, the L1 error of the corresponding kernel estimate is not larger than 3 + e times the error of the estimate with the optimal smoothing factor plus a constant times Ov~--~-n/n, where n is the sample size, and the constant only depends...
متن کاملA Characterization of the log density smoothing spline ANOVA model
In this paper we introduce a characterization of the log density smoothing spline ANOVA model. We show that in a log density ANOVA model of order r (consisting of the main effects and all the interactions of order up to r), the joint density function is uniquely determined by the collection of all r dimensional marginal densities. Furthermore, the order r model is the largest log density ANOVA ...
متن کاملFuzzy Shrink Image denoising using smoothing spline estimation
The image data is normally corrupted by additive noise during acquisition. This reduces the accuracy and reliability of any automatic analysis. For this reason, denoising methods are often applied to restore the original image. In proposed method a wavelet shrinkage algorithm based on fuzzy logic and the DT-DWT scheme is used. In particular, intra-scale dependency within wavelet coefficients is...
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: 1993
ISSN: 0090-5364
DOI: 10.1214/aos/1176349023