The mathematical model of such a process can be thought of as an inverse percolation process. Studying the effects of adversarial examples on neural networks can help researchers determine how their models could be vulnerable to unexpected inputs in the real world. When building forecasting models in Excel robustness is more important than accuracy. (To put an example: much of physics focuss on near equilibrium problems, and stability can be described very airily as tending to return towards equilibrium, or not escaping from it – in statistics there is no obvious corresponding notion of equilibrium and to the extent that there is (maybe long term asymptotic behavior is somehow grossly analogous) a lot of the interesting problems are far from equilibrium (e.g. Well, that occurred to us too, and so we did … and we found it didn’t make a difference, so you don’t have to be concerned about that.” These types of questions naturally occur to authors, reviewers, and seminar participants, and it is helpful for authors to address them. Is it not suspicious that I’ve never heard anybody say that their results do NOT pass a check? The other dimension is what I’m talking about in my above post, which is the motivation for doing a robustness check in the first place. For statistics, a test is robust if it still provides insight into a problem despite having its assumptions altered or violated. The principal categories of estimators are: (1) L-estimators that are adaptive or nonadaptive linear combinations of order statistics, (2) R-estimators are related to rank order tests, (3) M-estimators are analogs of maximum likelihood estimators, and (4) P-estimators that are analogs of Pitman estimators. Yet many people with papers that have very weak inferences that struggle with alternative arguments (i.e., have huge endogeneity problems, might have causation backwards, etc) often try to just push the discussions of those weaknesses into an appendix, or a footnote, so that they can be quickly waved away as a robustness test. is there something shady going on? The most extreme is the pizzagate guy, where people keep pointing out major errors in his data and analysis, and he keeps saying that his substantive conclusions are unaffected: it’s a big joke. As long as you can argue that a particular alternative method could be used to examine your issue, it can serve as a candidate for robustness checks in my opinion. It’s now the cause for an extended couple of paragraphs of why that isn’t the right way to do the problem, and it moves from the robustness checks at the end of the paper to the introduction where it can be safely called the “naive method.”. I like robustness checks that act as a sort of internal replication (i.e. So if it is an experiment, the result should be robust to different ways of measuring the same thing (i.e. Publisher Summary. Breaks pretty much the same regularity conditions for the usual asymptotic inferences as having a singular jacobian derivative does for the theory of asymptotic stability based on a linearised model. (In other words, is it a result about “people” in general, or just about people of specific nationality?). A systematic risk assessment is the major difference between the Eurocode robustness strategy of Class 3 buildings and that of Class 2b buildings. So it is a social process, and it is valuable. Such modifications are known as "adversarial examples." It’s typically performed under the assumption that whatever you’re doing is just fine, and the audience for the robustness check includes the journal editor, referees, and anyone else out there who might be skeptical of your claims. Regarding the practice of burying robustness analyses in appendices, I do not blame authors for that. ‘My pet peeve here is that the robustness checks almost invariably lead to results termed “qualitatively similar.” That in turn is of course code for “not nearly as striking as the result I’m pushing, but with the same sign on the important variable.”’ It can be used in a similar way as the anova function, i.e., it uses the output of the restricted and unrestricted model and the robust variance-covariance matrix as argument vcov. Correct. But, there are other, less formal, social mechanisms that might be useful in addressing the problem. The distribution of the product often requires manufacturing and packaging in multiple countries and locations. By far the most Based on the variance-covariance matrix of the unrestriced model we, again, calculate … How Are the Statistics of Political Polls Interpreted? Eg put an un-modelled change point in a time series. Considerations for this include: In most cases, robustness has been established through technical work in mathematical statistics, and, fortunately, we do not necessarily need to do these advanced mathematical calculations in order to properly utilize them; we only need to understand what the overall guidelines are for the robustness of our specific statistical method. http://www.theaudiopedia.com What is ROBUSTNESS TESTING? It’s better than nothing. Is there any theory on what percent of results should pass the robustness check? I often go to seminars where speakers present their statistical evidence for various theses. (Yes, the null is a problematic benchmark, but a t-stat does tell you something of value.). For example, maybe you have discrete data with many categories, you fit using a continuous regression model which makes your analysis easier to perform, more flexible, and also easier to understand and explain—and then it makes sense to do a robustness check, re-fitting using ordered logit, just to check that nothing changes much. There are other routes to getting less wrong Bayesian models by plotting marginal priors or analytically determining the impact of the prior on the primary credible intervals. In general the condition that we have a simple random sample is more important than the condition that we have sampled from a normally distributed population; the reason for this is that the central limit theorem ensures a sampling distribution that is approximately normal — the greater our sample size, the closer that the sampling distribution of the sample mean is to being normal. In the OFAT approach, only one factor is changed with all the others unchanged, and so the effect of changing that factor can be seen. Good question. Are we constantly chasing after these population-level effects of these non-pharmaceutical interventions that are hard to isolate when there are many good reasons to believe in their efficacy in the first instance? This usually means that the regression models (or other similar technique) have included variables intending to capture potential confounding factors. The purpose of a risk assessment is to determine whether there are any hazard scenarios that have an unacceptable level of risk and if so to identify steps to mitigate those risks. Such honest judgments could be very helpful. I don’t know. ), I’ve also encountered “robust” used in a third way: For example, if a study about “people” used data from Americans, would the results be the same of the data were from Canadians? Formalizing what is meant by robustness seems fundamental. You paint an overly bleak picture of statistical methods research and or published justifications given for methods used. This may be a valuable insight into how to deal with p-hacking, forking paths, and the other statistical problems in modern research. I realize its just semantic, but its evidence of serious misplaced emphasis. In many papers, “robustness test” simultaneously refers to: The goal is to create a model that helps you make informed decisions and understand the … Also, the point of the robustness check is not to offer a whole new perspective, but to increase or decrease confidence in a particular finding/analysis. Robustness checks involve reporting alternative specifications that test the same hypothesis. Many models are based upon ideal situations that do not exist when working with real-world data, and, as a result, the model may provide correct results even if the conditions are not met exactly. How to think about correlation? Learn more. measures one should expect to be positively or negatively correlated with the underlying construct you claim to be measuring). In both cases, I think the intention is often admirable – it is the execution that falls short. How to Determine the Validity and Reliability of an Instrument By: Yue Li. It can be useful to have someone with deep knowledge of the field share their wisdom about what is real and what is bogus in a given field. Maybe what is needed are cranky iconoclasts who derive pleasure from smashing idols and are not co-opted by prestige. 2. If robustness checks were done in an open sprit of exploration, that would be fine. (I’m a political scientist if that helps interpret this. I find them used as such. True story: A colleague and I used to joke that our findings were “robust to coding errors” because often we’d find bugs in the little programs we’d written—hey, it happens!—but when we fixed things it just about never changed our main conclusions. If R1 contains n data elements and k = the largest whole number ≤ np/2, then the k largest items and the k smallest … The variability of the effect across these cuts is an important part of the story; if its pattern is problematic, that’s a strike against the effect, or its generality at least. I blame publishers. Another social mechanism is bringing the wisdom of “gray hairs” to bear on an issue. But it isn’t intended to be. Although different robustness metrics achieve this transformation in different ways, a unifying framework for the calculation of different robustness metrics can be introduced by representing the overall transformation of f(x i, S) into R(x i, S) by three separate transformations: performance value transformation (T 1), scenario subset selection (T 2), and robustness metric calculation (T 3), as … That is, p-values are a sort of measure of robustness across potential samples, under the assumption that the dispersion of the underlying population is accurately reflected in the sample at hand. Some South American and Asian countries require in-country testing for marketed products. What you’re worried about in these terms is the analogue of non-hyperbolic fixed points in differential equations: those that have qualitative (dramatic) changes in properties for small changes in the model etc. First, robustness is not binary, although people (especially people with econ training) often talk about it that way. 2012), as it … The unstable and stable equilibria of a classical circular pendulum are qualitatively different in a fundamental way. Here’s the story: From the Archives of Psychological Science. Of course the difficult thing is giving operational meaning to the words small and large, and, concomitantly, framing the model in a way sufficiently well-delineated to admit such quantifications (however approximate). It’s a bit of the Armstrong principle, actually: You do the robustness check to shut up the damn reviewers, you have every motivation for the robustness check to show that your result persists . Those types of additional analyses are often absolutely fundamental to the validity of the paper’s core thesis, while robustness tests of the type #1 often are frivolous attempts to head off nagging reviewer comments, just as Andrew describes. Unlike MIT, Scientific American does the right thing and flags an inaccurate and irresponsible article that they mistakenly published. Robustness checks involve reporting alternative specifications that test the same hypothesis. Funnily enough both have more advanced theories of stability for these cases based on algebraic topology and singularity theory. It is not in the rather common case where the robustness check involves logarithmic transformations (or logistic regressions) of variables whose untransformed units are readily accessible. A pretty direct analogy is to the case of having a singular Fisher information matrix at the ML estimate. +1 on both points. Conclusions that are not robust with respect to input parameters should generally be regarded as useless. An outlier mayindicate a sample pecu… Robustness is determined by using either an experimental design or one factor at a time (OFAT). Other times, though, I suspect that robustness checks lull people into a false sense of you-know-what. Note: Ideally, robustness should be explored during the development of the assay method. Sometimes this makes sense. In the literature, robustness has been defined in different ways: - as same sign and significance (Leamer) - as weighted average effect (Bayesian and Frequentist Model Averaging) - as effect stability We define robustness as effect stability. Unfortunately, a field’s “gray hairs” often have the strongest incentives to render bogus judgments because they are so invested in maintaining the structure they built. We use a critical value of 2, as outlined in [8]. For more on the specific question of the t-test and robustness to non-normality, I'd recommend looking at this paper by Lumley and colleagues. Or Andrew’s ordered logit example above. Does including gender as an explanatory variable really mean the analysis has accounted for gender differences? T-procedures function as robust statistics because they typically yield good performance per these models by factoring in the size of the sample into the basis for applying the procedure. Sensitivity to input parameters is fine, if those input parameters represent real information that you want to include in your model it’s not so fine if the input parameters are arbitrary. I don’t think I’ve ever seen a more complex model that disconfirmed the favored hypothesis being chewed out in this way. 2 robust— Robust variance estimates If you wish to program an estimator for survey data, then you should write the estimator for nonsurvey data first and then use the instructions in[P] program properties (making programssvyable) to get your estimation command to work properly with the svy prefix. All of these manufacturing scenarios require transferring … It helps the reader because it gives the current reader the wisdom of previous readers. Perhaps “nefarious” is too strong. This sort of robustness check—and I’ve done it too—has some real problems. Are better left apart “ gray hairs ” to bear on a paper ’ s the story: from Archives. Check, is to take a set of data that we have sampled from is normally.! Can become the norm rarely specified not addressed with robustness checks is resistant to errors in the election! 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