Multivariable fractional polynomial method for regression model
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 کاملMultivariable Least Squares Frequency Domain Identification for Models described by Fractional Polynomial Descriptions
A commonly used approach in the identification of linear models is the usage of complex frequency domain data for performing a curve fit procedure to obtain a transfer function of a linear model that approximates the frequency domain data using e.g. a least squares minimization (Pintelon et al. 1994). This approach is favourite especially in applications where huge amounts of noisy experimental...
متن کاملA Computational Meshless Method for Solving Multivariable Integral Equations
In this paper we use radial basis functions to solve multivariable integral equations. We use collocation method for implementation. Numerical experiments show the accuracy of the method.
متن کاملModel selection of polynomial kernel regression
Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm of model selection. The first aim of this paper is to develop a strategy to select these parameters. On one hand, based on the worst-case learning rate analysis, we sho...
متن کاملFractional imputation using regression imputation model
Consider a finite population of N elements identified by a set of indices U = {1, 2, ..., N}. Associated with each unit i in the population there is a study variable yi and a vector xi of auxiliary variables. Let A denote the set of indices for the elements in a sample selected by a set of probability rules called the sampling mechanism. Let the population quantity of interest be θN = ∑N i=1 yi...
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: Annals of Translational Medicine
سال: 2016
ISSN: 2305-5839,2305-5847
DOI: 10.21037/atm.2016.05.01