# Questions tagged [sensitivity-analysis]

Auxiliary methods intended to check if the outcome of an analysis strongly depends on the model assumptions, preprocessing steps, presence of outliers, etc.

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### Using lm() for sensitivity analysis on scaled predictors [closed]

I have two vectors (X1, X2) that I'm using to estimate a new (latent) variable Y that is Y = X1*X2. I'd like to do a sensitivity analysis that assesses the importance of X1 or X2 to variance in Y. My ...
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### Exclusion of participants based on sensitivity and criterion (SDT)

When I check the histogram of the participants' bias (measured by c), the distribution is right-skewed (i.e., more values are less than zero as expected). This indicates that the participants were ...
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### Trade-off between omitting variables or dropping observations in multivariate logistic regression

Say you are selecting $n$ observations from a complex survey of $N$ individuals to create an analytical sample of relevant observations; and that you intend to fit a binomial multivariate logistic ...
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### What-If Scenario Regression Modelling

I'm pondering a scenario involving some insurance data but this could be relevant in many fields. The idea is that I have a total count of some event. Let's imagine this count is the # of attorney ...
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### How to analyze sensitivity of an optimizer to initial parameter values?

Is there a standard way to analyze sensitivity of an optimizer (like gradient descent) to initial value of the parameters? Some objective functions may have diverse local optima and by starting from ...
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### what is the sensitivity of the neural network using standardized input

Suppose I trained a neural network with standardisation of the data following (X-EX)/std(X). The input is x(t) and output is y(t). How can I calculate the sensitivity of this trained network (...
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### Can I perform sensitivity analysis, if I don't know expected prediction results?

Can I perform sensitivity analysis, if I don't know expected prediction results? I.e. I have a model with input parameters and weights. But I don't know when a prediction should be true and when false....
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### How do you deal with A/B testing for small samples?

I am performing A/B testing (basically hypothesis testing) with relatively small samples, so the results are largely inconclusive. I am aware of techniques like CUPED (for decreasing the sample ...
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### Application and confidence interval of McNemar test in comparing methods?

Given a known cohort (say all male) of 1510 people, if method A and method B are applied to predict M/...
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### Difference-in-Difference regression model for sensitivity analysis

I have 5-year sales information from a grocery store in Canada. I want to check whether an event that happened in 2017, affected the effect of the price of a product on its sales. For example, imagine ...
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### Estimating forecasting error in multi-step process

Suppose a process which looks something like this time series 1 --> model 1 --> time series 2 --> model 2 --> time series 3 An initial time series, which is a forecast, is used as input ...
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### How many percentage to randomize and how many iterations in a “what-if analysis”?

I've got complete separated data as such: ...
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### Sensitivity analysis after multiple imputation

After reviewing the literature there seems to be little consensus regarding the best way of performing sensitivity analysis following multiple imputation for missing values. However, the growing ...
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### Roadmap to Variance-based methods of sensitivity analysis

So I haven't had a statistics course since high school, but I now need to apply the Sobol method in an ODE model. I believe I need to learn a lot of statistics. Anyone, please give me a roadmap of ...
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### Using importance sampling for prior sensitivity analysis in Bayesian modeling

I read a section on Bayesian sensitivity analysis in the following book by Carlin and Louis (2009), 'Bayesian Methods for Data Analysis' (3rd ed.), CRC Press. The context is a sensitivity analysis of ...
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### Analyse sensitivity of hyper-parameters of Machine Learning Models

I want to analyse how sensitive my non neural net machine learning models are to the choice of the different parameters. I am currently using grid search to tune the models. Is there any method that I ...
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### Global sensitivity Morris method - choice of delta and normalisation of the elementary effects

I have few questions regarding the Morris method (as decribed e.g. in Campolongo, Cariboni, Saltelli, Environmental Modelling & Software 22, 2007 or Wenthworth et al. J. Uncertainty Quantification ...
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### comparing sensitivities

We want to compare two sampling methods. All patients will go through a DNA test for diagnosis(positive or negative). Then samples are taken to identify the determine the type of bacteria and ...