Sensitivity analysis refers to methods to see if violations of assumptions of a model make large differences to results.

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Can I apply d' prime for my accent rating task?

In my accent rating task, I asked listeners to listen to sounds which are produced by either native or non-native speakers and judge the degree of native-likeness based on nine-point scale. Each ...
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Is there are better way of solving this problem than using a Neural Network Regression Model?

I'm working on power plant time series data from a SO2 absorption process and my main objective is finding out which independent variables are critical for reducing SAG (% of SO2 concentration ...
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reason codes for non-linear models?

I have a non-linear model with n variables (ANN model). The variables are WOE-transformed to train the model. I have a test record scored using the non-linear model mentioned above and it is in the ...
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Positive or negative effect of neural network inputs on output in binary classification (MATLAB)?

How we can find an input has positive or negative effects on output in a binary classification neural network in MATLAB R2015a? (with ...
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Partial inclination coefficient for black box model?

I have a complex non-linear model that takes 7 parameters and produces an output. I wanted to perform global sensitivity analysis on these 7 parameters to quantify their relative importance on the ...
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1answer
52 views

Disadvantages of uncertainty in modeling

I am preparing a presentation, my work mainly concentrates on uncertainty and sensitivity analysis. I was wondering if I can convince my audience by the importance of studying uncertainty in modeling. ...
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22 views

Sensitivity analysis of computationally expensive model

I have a finite element model of a geometric structure which is computationally expensive to solve. The model is parameterized with 3 parameters. Each configuration is solved for an increasing load ...
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199 views

Sensitivity Analysis used in SPSS Neural Network Package

What is the sensitivity analysis used in SPSS Neural Network's independent variable importance calculation? The explanation provided by SPSS is very vague. Is it the same as the semipartial ...
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Sensitivity analysis of machine learning techniques

As you know we can have sensitivity analysis (sensitivity of output(s) based on changing of inputs) in different kinds of regression. Can we have sensitivity analysis for machine learning techniques ...
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Sensitivity Analysis of MA Process

Can sensitivity analysis be carried out for a time series moving average process? In a time series process, we wish to shock the x variables and beta coefficients and observe the effect on y. But in ...
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42 views

Sensitivity of Evolutionary algorithms to underlying random number generators

Techniques of evolutionary algorithms (EA) rely heavily on the use of random number generators (RNGs). From initial population generation, through the specific canonical operators applied to create ...
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245 views

How to perform regression with a sensitivity analysis in R

Without using non-base packages like plm, how can I perform a fixed effects regression in R with a sensitivity analysis for one or several other variables? Some ...
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Assessing predictor contribution to model output

Many of machine learning methods are considered as "black boxes". Examples of such methods are SVM, Neural Networks, Random forests etc. One may apply sensitivity analysis techniques (as described for ...
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Is there a common way to perform a sensitivity analysis for an Accelerated Failure Time model (AFT) with hundreds - thousands or parameters?

My model has hundreds to thousands of parameters (banking). We are required to deliver an uncertainty analysis part of which is a sensitivity analysis. Is this feasible? Recommended? Are there any ...
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Sensitivity Analysis for Mulitple Dependent and Independent Variables

I have Mulitple Vectors of Independent Variables and Multiple Vectors of Dependent Variables from a Monte Carlo Simulation. For each Dependent Variable, I want to know the contribution of each ...
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2answers
91 views

Match Right Skewed Distribution to Normal

I am running a simulation. One of my parameters is sampled from a normal distribution. I would like to perform a sensitivity analysis using a right skewed distribution. This is what I had hoped to ...
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195 views

What are the proper ways how to add noise to a dataset? (algorithm data sensitivity analysis)

Consider: dataset defined as n datapoints x_i in m-dimensional space. And there is a label ...
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82 views

Dealing with poorly estimated/missing explanatory variable values in GLMs

Context I am using generalised linear models to analyse some ecological data looking at the relationship between the population density of moth larvae and the prevalence (%) of viral mortality in the ...
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316 views

Differences between robustness checks and sensitivity analysis

This is a bit of a terminology question, but what is the difference between a robustness check and a sensitivity analysis? For example, if performing analysis to see how sensitive (or robust) a ...
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93 views

Selecting uncorrelated samples from a set of bulk data that contains correlated and dependent samples

i have a set of data that is generated by expensive computational model evaluations, on a total data set of 10000 samples in 40 dimensions. This sample data set is composed of different data sets, ...
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Notation conditioned means for calculation variance based first and total order effect

Could someone maybe give an explanation and formulas in terms of sums or matrix operations for the calculation of variance based effects, and total effect indices? The information given on wikipedia: ...
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How to evaluate a regression model's sensitivity to noise

How can I investigate the sensitivity of a regression model to noise? A basic idea is to add some (Gaussian) noise to the dependent and/or independent variables and (re)evaluate the RMSE. However, ...
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beta distributions assigned to represent uncertainty

I need to calculate the Probabilistic Sensitivity Analysis for a function. I was given this: beta distributions assigned to represent uncertainty And have this parameter with this data: ...
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Difference between Sobol indices and total Sobol indices?

Given a mathematical model $Y\widetilde Y(X_i)$, where $X_i=x_i^*$ represents a particular point estimate for input variable $X_i$. In sensitivity analysis, Sobol indices explain the importance of an ...
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How to prove it's not just a chance finding when testing several variables

If I tested several similar variables, such as socioeconomic status measures, but only a few were significant, how would I prove that it's not just a chance finding? Besides having the literature to ...
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ROC and multiROC analysis: how to calculate optimal cutpoint?

I'm trying to understand how to compute the optimal cut-point for a ROC curve (the value at which the sensitivity and specificity are maximized). I'm using the dataset ...
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How to run a sensitivity analysis with dependent variables?

I am trying to run a sensitivity analysis on an efficiency indicator: the ratio between nitrogen outputs (milk, meat, crops) and nitrogen inputs (fertilizers, cattle feed, symbiotic fixation…). I ...
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127 views

How does Soboljansen deal with categorical input against continuous output?

I have two data.tables which correspond to each other: ...
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135 views

Meta-analysis and homogeneity — what did these guys do?

I appreciate any insight into this meta-analysis. This is a meta-analysis on alogliptin efficacy and safety. In the 2nd paragraph of the discussion that make this statement: Although ...
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55 views

Local sensitivity around the maximum

After having inferred a statistical model for my physical system $Y=f(X_1, \dots X_n)$, I want to estimate the local sensitivity of each variable $X_i$ (one by one) around the point of maximum ...
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521 views

Sensitivity analysis for modeled time series

I have very basic knowledge of stats so my question may sound very simplistic. I have a large time series of measured data and have calibrated a model with five parameters to make predictions. I would ...
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174 views

Linearity in local sensitivity analysis

As it is know local sensitivity analysis attempt to quantify the local impact of input factors on the model, through partial derivatives: a derivative of the outputs accordingly to the inputs, when ...
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1k views

Interpreting results from Sobol sensitivity analysis in R

I'm trying to use the sobol2007 model in the R sensitivity package. I'm doing runs on a model with 26 parameters, and using 2 sets of 500 monte-carlo samples to seed the analysis, and nboot=500. This ...
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358 views

Using fast99 sensitivity method in R with normal distributions

If I use the fast99 method (from the sensitivity package) with qnorm, it generates parameter sets with -Inf and Inf in, which are not much use for then running models: ...
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Literature on robustness of regression assumptions

In my OLS regression not all assumptions are perfectly met, but I read that due to a large sample size there is a certain robustness to assumptions (my sample is 2500 people). E.g. the DV isn't ...