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Best (but oft-forgotten) practices: sensitivity analyses in randomized controlled trials
analysis commentary sensitivity statistics tutorial
2018/11/23
Arandomized controlled clinical trial is the best way to minimize bias in ascertaining treatment effects, but the credibility of the results of a trial depends on the validity of the methods used to a...
On Achieving Reduced Error Propagation Sensitivity in DFE Design via Convex Optimization
On Achieving Reduced Propagation Sensitivity DFE Design via Convex Optimization
2015/7/10
Decision Feedback Equalization (DFE) is expected in digital TV receivers and other high error rate environments. Error propagation usually occurs in infrequent bursts. It is argued here that the minim...
HRF estimation improves sensitivity of fMRI encoding and decoding models
fMRI hemodynamic HRF GLM BOLD en-coding decoding
2013/6/14
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) sig...
In a model of the form $Y=h(X_1,\ldots,X_d)$ where the goal is to estimate a parameter of the probability distribution of $Y$, we define new sensitivity indices which quantify the importance of each v...
Goal-oriented error estimation for reduced basis method, with application to certified sensitivity analysis
reduced basis method surrogate model reduced order modelling re-sponse surface method scientific computation sensitivity analysis Sobol index computation Monte-Carlo method
2013/5/2
The reduced basis method is a powerful model reduction technique designed to speed up the computation of multiple numerical solutions of parameterized partial differential equations (PDEs). We conside...
Generalized Sobol sensitivity indices for dependent variables: numerical methods
Dependent variables Extended basis Greedy algorithm LARS Sensitivity analysis Sobol decomposition
2013/4/28
The hierarchically orthogonal functional decomposition of any measurable function f of a random vector X=(X_1,...,X_p) consists in decomposing f(X) into a sum of increasing dimension functions dependi...
We define and study a generalization of Sobol sensitivity indices for the case of a vector output.
Global sensitivity analysis of a numerical code, more specifically estimation of Sobol indices associated with input variables, generally requires a large number of model runs.
Reproducing kernels for spaces of zero mean functions. Application to sensitivity analysis
Kernel Methods Global Sensitivity Analysis Sobol-Hoeffding Decomposition
2011/7/6
Given a Reproducing Kernel Hilbert Space (H, h., .i) of real-valued functions and a suitable measure \mu over the source space, we decompose H as sum of a subspace of centered functions for {\mu} and ...
Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods
random matrix related statistical shrinkage estimators
2011/6/16
Shrinkage estimators of covariance are an important tool in modern applied and theoretical statistics.
They play a key role in regularized estimation problems, such as ridge regression (aka Tykhonov
...
Confidence intervals for sensitivity indices using reduced-basis metamodels
sensitivity analysis reduced basis method Sobol indices bootstrap method Monte Carlo method
2011/3/24
Global sensitivity analysis is often impracticable for complex and time demanding numerical models, as it requires a large number of runs. The reduced-basis approach provides a way to replace the orig...
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal inference causal mediation analysis direct and indirect effects linear structural equation models
2010/11/9
Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles...
The Sensitivity of Respondent-driven Sampling Method
directed network hidden population network respondent-driven sampling RDS sensitivity
2010/3/10
Researchers in many scientific fields make inferences from individuals to larger groups. For many groups however,there is no list of members from which to take a random sample. Respondent-driven sampl...
On the role of contamination level and the least favourable bahaviour of gross-error sensitivity
the role of contamination level the least favourable bahaviour gross-error sensitivity
2009/9/23
The notion of contamination level is introduced and its
characterization for any pair of distribution functions is given.
A possibility of reformulation of some basic problems of the robust
statist...
Sensitivity of principal Hessian direction analysis
dimension reduction influence function influential observations principal hessian directions
2009/9/16
We provide sensitivity comparisons for two competing versions of the dimension reduction method principal Hessian directions (pHd). These comparisons consider the effects of small perturbations on the...