搜索结果: 1-12 共查到“理论统计学 Framework”相关记录12条 . 查询时间(0.092 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
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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 General Framework for Structured Sparsity via Proximal Optimization
General Framework Structured Sparsity Proximal Optimization
font style='font-size:12px;'>
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...
Threshold estimation based on a p-value framework in dose-response and regression settings
estimation based p-value framework dose-response regression settings
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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
font style='font-size:12px;'>
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...
SMART：A statistical framework for optimal design matrix generation with application to fMRI
statistical framework optimal design matrix generation fMRI
font style='font-size:12px;'>
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...
A Bayesian framework for estimating vaccine efficacy per infectious contact
Vaccine efficacy Bayesian MCMC measurement error copula
font style='font-size:12px;'>
2010/3/17
In vaccine studies for infectious diseases such as human immunodeficiency
virus (HIV), the frequency and type of contacts between
study participants and infectious sources are among the most informa...
A Bayesian Framework for Combining Valuation Estimates
value investing financial statement analysis equity valuation
font style='font-size:12px;'>
2010/4/30
Obtaining more accurate equity value estimates is the starting point for stock
selection, value-based indexing in a noisy market, and beating benchmark indices
through tactical style rotation. Unfor...