Variable Selection and Parameter Estimation with the Atan Regularization Method
نویسندگان
چکیده
منابع مشابه
Bayesian variable selection and regularization for time–frequency surface estimation
We describe novel Bayesian models for time–frequency inverse modelling of nonstationary signals. These models are based on the idea of a Gabor regression, in which a time series is represented as a superposition of translated, modulated versions of a window function exhibiting good time–frequency concentration. As a necessary consequence, the resultant set of potential predictors is in general ...
متن کاملAn Iterative Method for the Solution of Nonlinear Regularization Problems with Regularization Parameter Estimation
Ill posed problems constitute the mathematical model of a large variety of applications. Aim of this paper is to define an iterative algorithm finding the solution of a regularization problem. The method minimizes a function constituted by a least squares term and a generally nonlinear regularization term, weighted by a regularization parameter. The proposed method computes a sequence of iterat...
متن کاملAutomatic estimation of regularization parameter by active constraint balancing method for 3D inversion of gravity data
Gravity data inversion is one of the important steps in the interpretation of practical gravity data. The inversion result can be obtained by minimization of the Tikhonov objective function. The determination of an optimal regularization parameter is highly important in the gravity data inversion. In this work, an attempt was made to use the active constrain balancing (ACB) method to select the...
متن کاملTotal Variation Regularization and L-curve method for the selection of regularization parameter
.......................................................................................................... i
متن کاملBayesian Parameter Estimation and Variable Selection for Quantile Regression
The principal goal of this work is to provide efficient algorithms for implementing the Bayesian approach to quantile regression. There are two major obstacles to overcome in order to achieve this. Firstly, it is necessary to specify a suitable likelihood given that the frequentist approach generally avoids such specifications. Secondly, sampling methods are usually required as analytical expre...
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: Journal of Probability and Statistics
سال: 2016
ISSN: 1687-952X,1687-9538
DOI: 10.1155/2016/6495417