Robust meaning

• v • t • e In robustness is the ability of a computer system to cope with Introduction [ ] In general, building robust systems that encompass every point of possible failure is difficult because of the vast quantity of possible inputs and input combinations. Regardless, complex systems should still handle any errors encountered gracefully. There are many examples of such successful systems. Some of the most robust systems are evolvable and can be easily adapted to new situations. Challenges [ ] Programs and software are tools focused on a very specific task, and thus aren't generalized and flexible. Currently, computer science practices do not focus on building robust systems. Areas [ ] Robust programming [ ] Robust programming is a style of programming that focuses on handling unexpected termination and unexpected actions. Principles [ ] Paranoia When building software, the programmer assumes users are out to break their code. Stupidity The programmer assumes users will try incorrect, bogus and malformed inputs. Dangerous implements Users should not gain access to Very often, code is modified and may introduce a possibility that an "impossible" case occurs. Impossible cases are therefore assumed to be highly unlikely instead. Robust machine learning [ ] Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, o...

Contents • 1 English • 1.1 Etymology • 1.2 Pronunciation • 1.3 Adjective • 1.3.1 Usage notes • 1.3.2 Derived terms • 1.3.3 Related terms • 1.3.4 Translations • 1.4 Further reading • 1.5 Anagrams • 2 Catalan • 2.1 Etymology • 2.2 Pronunciation • 2.3 Adjective • 2.3.1 Derived terms • 2.4 References • 2.5 Further reading • 3 German • 3.1 Etymology • 3.2 Pronunciation • 3.3 Adjective • 3.3.1 Declension • 3.3.2 Derived terms • 3.4 Further reading • 4 Norwegian Bokmål • 4.1 Etymology • 4.2 Adjective • 4.3 References • 5 Norwegian Nynorsk • 5.1 Etymology • 5.2 Adjective • 5.3 References • 6 Romanian • 6.1 Etymology • 6.2 Adjective • 6.2.1 Declension • 7 Swedish • 7.1 Adjective • 7.1.1 Declension • 7.1.2 References English [ ] Etymology [ ] Pronunciation [ ] • ( ) ( /ɹəʊˈbʌst/, /ɹəˈbʌst/ • Audio (UK) ( • Audio (UK) ( • ( ) ( /ɹoʊˈbʌst/ • Rhymes: -ʌst • Hyphenation: ro‧bust Adjective [ ] robust ( or or ( see usage notes ) • ( often, especially ) both robust employment • • ( ) Designed or evolved in such a way as to be resistant to total failure despite partial damage. • ( ) Resistant or impervious to failure regardless of user input or unexpected conditions. • ( ) Not greatly influenced by errors in assumptions about the distribution of sample errors. • ( chiefly , , ) Of an individual or skeletal element: strongly built; Usage notes [ ] • "More" and "most robust" are much more common than the forms ending in "-er" or "-est". Derived terms [ ] • Related terms [ ] • Translations [ ]...

(adj.) When used to describe robustcan describe one or more of several qualities: • a system that does not break down easily or is not wholly affected by a single • a system that either recovers quickly from or holds up well under exceptional circumstances • a system that is not wholly affected by a • a system that comes with a wide range of capabilities. (It should be noted that this last sense of the term robust is not uniformly accepted in technical circles. The term is typically used in this sense in the marketing of software or computer systems to emphasize a selling point and does not refer to the first three meanings of the term.) Webopedia is an online information technology and computer science resource for IT professionals, students, and educators. Webopedia focuses on connecting researchers with IT resources that are most helpful for them. Webopedia resources cover technology definitions, educational guides, and software reviews that are accessible to all researchers regardless of technical background. Property of TechnologyAdvice. © 2022 TechnologyAdvice. All Rights Reserved Advertiser Disclosure: Some of the products that appear on this site are from companies from which TechnologyAdvice receives compensation. This compensation may impact how and where products appear on this site including, for example, the order in which they appear. TechnologyAdvice does not include all companies or all types of products available in the marketplace.

What are robust standard errors? How do we calculate them? Why use them? Why not use them all the time if they’re so robust? Those are the kinds of questions this post intends to address. To begin, let’s start with the relatively easy part: getting robust standard errors for basic linear models in Stata and R. In Stata, simply appending vce(robust) to the end of regression syntax returns robust standard errors. “vce” is short for “variance-covariance matrix of the estimators.” “robust” indicates which type of variance-covariance matrix to calculate. Here’s a quick example using the Linear regression Number of obs = 74 F(2, 71) = 68.76 Prob > F = 0.0000 R-squared = 0.5521 Root MSE = 3.926 ------------------------------------------------------------------------------ | Robust mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- turn | -.7610113 .1447766 -5.26 0.000 -1.049688 -.472335 trunk | -.3161825 .1229278 -2.57 0.012 -.5612936 -.0710714 _cons | 55.82001 4.866346 11.47 0.000 46.11679 65.52323 Notice the third column indicates “robust” standard errors. To replicate the result in R takes a bit more work. First we load the haven package to use the read_dta() function that allows us to import Stata data sets. Then we load two more packages: lmtest and sandwich. The lmtest package provides the coeftest() function that allows us to re-calculate a coefficient table using a different variance-covariance...