Kernel-penalized regression for analysis of microbiome data
نویسندگان
چکیده
منابع مشابه
Nonparametric Regression Estimation under Kernel Polynomial Model for Unstructured Data
The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...
متن کاملSemi-supervised Penalized Output Kernel Regression for Link Prediction
Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vectorvalued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance using numerical experi...
متن کاملWavelet kernel penalized estimation for non-equispaced design regression
The paper considers regression problems with univariate design points. The design points are irregular and no assumptions on their distribution are imposed. The regression function is retrieved by a wavelet based reproducing kernel Hilbert space (RKHS) technique with the penalty equal to the sum of blockwise RKHS norms. In order to simplify numerical optimization, the problem is replaced by an ...
متن کاملPenalized likelihood regression in reproducing kernel Hilbert spaces with randomized covariate non-Gaussian data
An Appendix with proofs and tuning details has been added here. Abstract Classical penalized likelihood regression problems deal with the case that the independent variables data are known exactly. In practice, however, it is common to observe data with incomplete covariate information. We are concerned with a fundamentally important case where some of the observations do not represent the exac...
متن کاملPenalized Likelihood Regression in Reproducing Kernel Hilbert Spaces with Randomized Covariate Data
Penalized likelihood regression consists of a category of commonly used regularization methods, including regression splines with RKHS penalty and the LASSO. When the observed data comes from a non-Gaussian exponential family distribution, a penalized log-likelihood is commonly used to estimate of the regression function. This technique allows a flexible form of the estimator and aims at an app...
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 Applied Statistics
سال: 2018
ISSN: 1932-6157
DOI: 10.1214/17-aoas1102