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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663 views

Difference between Sensitivity analysis and Design of Experiments

Reading on wikipedia about the methods for sensitivity analysis: different methods are stated. At the end of the wikipedia page, a section called Related Concepts speaks about Design of experiments (...
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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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Metafor package: bias and sensitivity diagnostics

I am conducting a multi-level meta-analysis that includes some articles with multiple outcomes. Therefore I am using the rma.mv() function. Example code: ...
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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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672 views

Sobol variance based decomposition

I have 6 input variables, each of which is normally distributed. Can I use Sobol variance-based sensitivity analysis? I have read some articles where they said that input variables must have uniform ...
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230 views

What does it mean if the Sobol main and total effects indices are the same?

What does it mean when the total and main effects ANOVA indices are the same? Does it mean there is zero interaction of the different inputs? Is there some other way to quantify or understand that? I ...
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227 views

Can first order sensitivity indices be greater than the total order sensitivity indices?

I am using variance based sensitivity analysis method from SAFE toolbox in matlab to determine the first order (Si) and total order sensitivity indices (STi). Theoretically, the STi is either equal to ...
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159 views

Regression sensitivity analysis by re-sampling duplicates

In R, I have completed a simple regression of the form lm(Y~x+a). The dataset original dataset included several non-independent data points, which I selected among using a set of rules. I want to test ...
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281 views

What do the specific values of the Sobol' indices mean?

I understand that first order and total effect Sobol' indices demonstrate the relative importance of the input parameters on the output of a given model. My question is, do the specific values of each ...
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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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Which approach should be used to compare two different measurement techniques of same samples?

I have individually measured failure forces of 8 materials and those recorded with A method and B method in same time: 8 results in each method, A=8 and B=8. The range of data of both measurement ...
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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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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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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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How to interpret relative sensitivity function calculated from first derivative in local sensitivity analysis?

I am trying to do sensitivity analysis on a given function by calculating the 1st-order (partial) derivatives. In a simple case let's say the function is $y=ax^2$. Then the 1st-order derivative is: $\...
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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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How to calculate Spearman's coefficient in a NPV simulation?

I did a Monte Carlo simulation on the net present value (NPV) of an investment. The input variables are: initial investment cost ($I_0$) output volume of year $t$ ($Q_t$) output price of year $t$ ($...
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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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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 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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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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one way sensitivity analysis with categorical variables

I am studying the impact of different factors on the expected price change for drugs. I have a regression model that looks like the following: Y=a+B1x1+B2x2... I would like to conduct some one-way ...
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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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Sobol indices of dependent uniform bivariates with resampling

Context We have a black-box problem modelized by a function $Y$ with $n$ inputs $X_1,\dots,X_n$ and generates a value $Y=f(X_1,\dots,X_n)$ for a number $N_S$ of samples. We aim to calculate $S_i = \...
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Can logistic regression be used to analyze sensitivity of a test over time?

I am working with a data sets to compare two clinical tests that yield either a positive result (1) or negative result (0). The clinical tests use EEG responses, so as the testing time increases, the ...
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Best practices for calculating Sobol Indices for time-series data

I'm looking to implement a global sensitivity analysis (GSA) study with what I'll treat as a black-box simulation. My main inspiration is this paper where they use the Sobol indices as their GSA ...
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Sensitivity analysis using R's mice package with multiple missing variables

I am using mice to multiply impute data on a dataset with many variables with missing values. I followed this vignette to do a sensitivity analysis to understand how the imputations are influenced by ...
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Interpretation of regression coefficients after power transform (possibily with polynomial transform and PCA) [duplicate]

In general I standardize my features before regression by subtracting the mean and dividing by unit variance: $$ \hat{X} = \frac{X - \bar{X}}{Var(X)}$$ With this basic standardization, interpreting ...
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Mean and Median properties

Can somebody explain me clear the mathematical logic that would link two statements (a) and (b) together? Let us have a set of values (some distribution). Now, a) Median does not depend on every ...
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How to do ANOVA analysis for therapeutic biomarker screening for drug sensitivity

I am new to Bioinformatics. The graph below (from GDSC database) is to find therapeutic biomarker for drug sensitivity. I wonder how to do this kind of ANOVA analysis on my own so as to make this kind ...
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What is the name for this type of sensitivity measure in regression analysis?

I have a way of calculating sensitivity of a regression that is very useful for my particular domain, but I don't know what it is called. I would like a name for it so I can look up additional ...
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158 views

Implementation of the weighted sum model without knowing the weights

I am looking to combine n metrics to obtain 1 single unified metric. For example, let's say I have 2 metrics n1 and n2 for k elements. I am particularly interested in the one or two elements that have ...
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Metafor package: choice of moderator for bias correction

I am doing a 3-level meta-analysis using rma.mv() function. I found a thread about bias diagnostics with an advice to use "regression test for funnel plot ...
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1answer
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Bayes theorem calculation not giving proper result when calculating the positive predictive value

I have following data from experiment to assess accuracy of test in patients with and without disease: Where T stands for test and D stands for disease. I want to get probability of (D+|T+) with ...
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Intuition for Sobol Indices

I have recently been learning about Sobol indices and have found them quite informative. However, as I have explored them I have encoutnered situations where I have found them counter intuitive and in ...
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Local sensitivity analysis with exponential and uniform distributions as input

For my thesis I need to run a sensitivity analysis on the input factors for a supply chain model. I am supposed to change the mean and the standard deviation (sd) of all input factors respectively by ...
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Estimating parameters of Virial equation using Ordinary Least Squares

I tried to estimate the positive parameters $B$ and $C$ of the Virial equation $pV=\bar{n}(RT+Bp+Cp^2)$, where $\bar{n}=0.25, \, T=300, \, R=8.314$ with the data $$\begin{align} p &= (50,60,\...
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Sensitivity Analysis in Deep Neural Networks

Following a question already answered (Extracting weight importance from One-Layer feed-forward network) I am looking for inference about relevance of inputs in neural networks. Considering a deep ...
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1answer
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How to interpret sensitivity power analyses?

Journals and reviewers increasingly ask authors to systematically report sensitivity power analyses. I know that a sensitivity power analysis allows you to determine the minimum effect size that the ...
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What are the sensitivity analyses for propensity score matching-based estimation?

I'm interested in using Propensity Score Matching (PSM) to create matched control vs treatment sample and estimate the treatment effect. But the problem with PSM is that the sample is matched based on ...
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Understanding Sobol in R Package Sensitivity

Sobol method quantifies the contributions of input variance to output variance. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by ...
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Standardized beta coefficients in nonlinear regression

In linear models $Y=X\beta+\epsilon$, where the errors $\epsilon_i\sim\text{Normal}(0,\sigma^2)$ are independent, the standardized beta coefficients are given by $$ \beta_i^*=\beta_i\frac{\sigma_{x_i}}...
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642 views

How to use sobol2002 for sensitivity analysis in R?

I have a very basic understanding of R and stat so my question may sound very simple. I am trying to do a sensitivity analysis on a model that takes 30+ input parameters. The model is created in R. ...
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What is the correct method for comparing sensitivity and specifity of different tests?

What is the correct method of comparing efficiency of different test for one sample of individuals? Are ROC and AUC enough? Comparing of sensitivity and specifity values of the tests with McNemar ...
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Probabilistic estimates of Kaplan-Meier curves using S(t) and std.err for each time point

I am working on a survival analysis where I am fitting different parametric models to survival data. Varying the models that I have fit to the data is straightforward, via Cholesky decompositions- but ...