搜索结果: 46-60 共查到“统计学 regression”相关记录243条 . 查询时间(0.037 秒)
An Approximate Approach to E-optimal Designs for Weighted Polynomial Regression by Using Tchebycheff Systems and Orthogonal Polynomials
An Approximate Approach E-optimal Designs Weighted Polynomial Regression Using Tchebycheff Systems Orthogonal Polynomials
2013/4/28
In statistics, experimental designs are methods for making efficient experiments. E-optimal designs are the multisets of experimental conditions which minimize the maximum axis of the confidence ellip...
Additive inverse regression models with convolution-type operators
Inverse regression Additive models Convolution-type operators Mathematical subject codes: primary 62G08 secondary 62G15 62G20
2013/4/27
In a recent paper Birke and Bissantz (2008) considered the problem of nonparametric estimation in inverse regression models with convolution-type operators. For multivariate predictors nonparametric m...
Iterative Isotonic Regression
Nonparametric statistics isotonic regression additive models met-ric projection onto convex cones
2013/4/28
This article introduces a new nonparametric method for estimating a univariate regression function of bounded variation. The method exploits the Jordan decomposition which states that a function of bo...
Regression with Distance Matrices
functional data analysis mixed data multidimensional scaling shape correlation ma-trix
2013/4/27
Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these...
Simultaneous L^2- and L^inf-Adaptation in Nonparametric Regression
Adaptive estimation nonparametric regression thresholding wavelets
2013/4/27
Consider the nonparametric regression framework. It is a classical result that the minimax rates for L^2- and L^inf-risk over a H\"older ball with smoothness index \beta are n^(-\beta/(2\beta+1)) and ...
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Profile regression Clustering Dirichlet process mixture model
2013/4/27
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vect...
Explaining temporal trends in annualized relapse rates in placebo groups of randomized controlled trials in relapsing multiple sclerosis: systematic review and meta-regression
multiple sclerosis relapses annualized relapse rates placebo baseline characteristics eligibility criteria meta-analysis meta-regression systematic review
2013/4/27
Background: Recent studies have shown a decrease in annualised relapse rates (ARRs) in placebo groups of randomised controlled trials (RCTs) in relapsing multiple sclerosis (RMS).
Methods: We conduc...
Complex Support Vector Machines for Regression and Quaternary Classification
Support Vector Machines Kernel methods Widely linear estimation com-plex data
2013/4/28
We present a support vector machines (SVM) rationale suitable for regression and quaternary classification problems that use complex data, exploiting the notions of widely linear estimation and pure c...
K-Nearest Neighbour algorithm coupled with logistic regression in medical case-based reasoning systems. Application to prediction of access to the renal transplant waiting list in Brittany
Case-based Reasoning systems logistic models similarity measures k-nearest neighbors algorithms classi-fication
2013/4/28
Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of sol...
Penalized Likelihood and Bayesian Function Selection in Regression Models
generalized additive model regularization smoothing spike and slab priors
2013/4/27
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...
Penalized Likelihood and Bayesian Function Selection in Regression Models
generalized additive model regularization smoothing spike and slab priors
2013/4/27
Challenging research in various fields has driven a wide range of methodological advances in variable selection for regression models with high-dimensional predictors. In comparison, selection of nonl...
On the convergence of the IRLS algorithm in Non-Local Patch Regression
Non-local means non-local patch regression,ℓ p minimization non-convex optimization iteratively reweighted least-squares majorize-minimize stationary point relaxation sequence linear convergence
2013/4/28
Recently, it was demonstrated in [CS2012,CS2013] that the robustness of the classical Non-Local Means (NLM) algorithm [BCM2005] can be improved by incorporating $\ell^p (0 < p \leq 2)$ regression into...
Second-Order Non-Stationary Online Learning for Regression
Second-Order Non-Stationary Online Learning for Regression
2013/4/28
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...
Model selection and estimation of a component in additive regression
Model selection estimation component additive regression
2012/11/23
Let $Y\in\R^n$ be a random vector with mean $s$ and covariance matrix $\sigma^2P_n\tra{P_n}$ where $P_n$ is some known $n\times n$-matrix. We construct a statistical procedure to estimate $s$ as well ...
Correlated variables in regression: clustering and sparse estimation
Canonical correlation group Lasso Hierarchical clustering High-dimensional inference Lasso Oracle inequality Variable screening Variable selection
2012/11/23
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as the Lasso for cluste...