搜索结果: 1-15 共查到“统计学 framework G-U-I-N”相关记录17条 . 查询时间(0.187 秒)
Asymptotic normality of a Sobol index estimator in Gaussian process regression framework
Sensitivity analysis Gaussian process regression asymptotic normality stochas-tic simulators Sobol index
2013/6/14
Stochastic simulators such as Monte-Carlo estimators are widely used in science and engineering to study physical systems through their probabilistic representation. Global sensitivity analysis aims t...
A new framework for optimal classifier design
Class Imbalance One Class SVM F-measure Recall Precision Fraud Detection Level Set Method
2013/6/14
The use of alternative measures to evaluate classifier performance is gaining attention, specially for imbalanced problems. However, the use of these measures in the classifier design process is still...
A framework to characterize performance of LASSO algorithms
Noisy linear systems of equations LASSO SOCP ℓ 1 -optimization compressed sensing
2013/5/2
In this paper we consider solving \emph{noisy} under-determined systems of linear equations with sparse solutions. A noiseless equivalent attracted enormous attention in recent years, above all, due t...
A Unified Framework for Probabilistic Component Analysis
A Unified Framework Probabilistic Component Analysis
2013/5/2
In this paper, we present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood via the prior, thus providi...
Unified theoretical framework for unit root and fractional unit root
First autoregression fractional integration explosive processes unit root fractional unit
2012/11/22
To distinguish between purely fractionally integrated (FI) processes, we propose in this article an appropriate fractional Dickey-Fuller test (F-DF). This extends the familiar Dickey-Fuller (1979) typ...
A stochastic variational framework for fitting and diagnosing generalized linear mixed models
Hierarchical model Identify divergent units Large longitudinal data Non-conjugate model Stochastic approximation Variational Bayes
2012/9/17
Variational Bayes computational methods are attracting increasing in-terest because of their ability to scale to large data sets. Here, application of the
non-conjugate variational message passing (N...
MMANOVA: A general multilevel framework for multivariate analysis of variance
Bayesian inference Constraints Mixed model Variance components
2012/9/19
Classical analysis of variance requires that model terms be labeled as xed or random and typically culminate by comparing variability from each batch (factor) to variability from errors; without a st...
A General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
2011/7/7
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimi...
A nonparametric empirical Bayes framework for large-scale multiple testing
Dirichlet process marginal likelihood mixture model
2011/7/6
We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-n...
A General Framework for Sequential and Adaptive Methods in Survival Studies
Sequential analysis adaptive allocation marked point process
2011/7/5
Adaptive treatment allocation schemes based on interim responses have generated a great deal of recent interest in clinical trials and other follow-up studies.
Threshold estimation based on a p-value framework in dose-response and regression settings
estimation based p-value framework dose-response regression settings
2011/7/5
We use p-values to identify the threshold level at which a regression function takes off from its baseline value, a problem motivated by applications in toxicological and pharmacological dose-response...
A Bregman Extension of quasi-Newton updates I: An Information Geometrical framework
A Bregman Extension quasi-Newton updates I An Information Geometrical
2010/10/19
We study quasi-Newton methods from the viewpoint of information geometry induced associated with Bregman divergences. Fletcher has studied a variational problem which derives the approximate Hessian u...
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
unified framework high-dimensional analysis $M$-estimators decomposable regularizers
2010/10/19
High-dimensional statistical inference deals with models in which the the number of parameters $p$ is comparable to or larger than the sample size $n$. Since it is usually impossible to obtain consist...
Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
Causal graphical models QTL mapping joint inference of phenotype network and genetic architecture systems genetics
2010/10/19
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be comple...
SMART：A statistical framework for optimal design matrix generation with application to fMRI
statistical framework optimal design matrix generation fMRI
2010/3/18
The general linear model (GLM) is a well established tool for analyzing functional magnetic resonance imaging (fMRI) data. Most fMRI analyses via GLM proceed in a massively univariate
fashion where t...