A robust model will continue to provide executives and managers with effective decision-making tools, and investors with accurate information on which to base their investment decisions. From the corporate executives of large multinational corporations to the franchise owner of the local burger restaurant, decision-makers need timely information presented to them in a model form that best reflects the activities of the business. Investors also use financial models to analyze and forecast the value of corporations to determine if they are viable prospective investments.
- Cyclical improvement creates a framework and environment that allows for continuous growth and greater efficiency over time.
- Blindly adding code introduces more errors, makes the system more complex, and renders it harder to understand.[6] Code that doesn’t provide any reinforcement to the already existing code is unwanted.
- For statistics, a test is robust if it still provides insight into a problem despite having its assumptions altered or violated.
- He specializes in using statistics in investing, technical analysis, and trading.
- For an example of robustness, we will consider t-procedures, which include the confidence interval for a population mean with unknown population standard deviation as well as hypothesis tests about the population mean.
Every process in a successful Six Sigma company has some robust elements, but robustness is not the only element of a strong process. Achieving the right balance requires leaders to keep the full scope of their operations in view when investing in certain processes or prioritizing changes. In the world of investing, robust is a characteristic describing a model’s, test’s, or system’s ability to perform effectively while its variables or assumptions are altered. A robust concept will operate without failure and produce positive results under a variety of conditions.
Very often, a trading model will function well in a specific market condition or time period. However, when market conditions change, or the model is applied to another time period or the future, the model fails horribly, and losses are realized. Business financial models focus mainly on the fundamentals of a corporation or business, such as revenues, costs, profits, and other financial ratios. A model is considered to be robust if its output and forecasts are consistently accurate even if one or more of the input variables or assumptions are drastically changed due to unforeseen circumstances. For example, a specific cost variable may sharply increase due to a severe decrease in supply resulting from a natural disaster.
More from Merriam-Webster on robust
Due to high demand at peak hours and small cooking surface, the operator cooks burgers at a high temperature for minimal time. Unfortunately, this increases the risk of overcooking or under-cooking the meat. For an https://www.globalcloudteam.com/ example of robustness, we will consider t-procedures, which include the confidence interval for a population mean with unknown population standard deviation as well as hypothesis tests about the population mean.
Robustness is also something that’s built over the course of trial and experience. Cyclical improvement creates a framework and environment that allows for continuous growth and greater efficiency over time. Robustness is a fundamental characteristic of a good process and one that will be successful in a lean manufacturing or operational environment.
One way to observe a commonly held robust statistical procedure, one needs to look no further than t-procedures, which use hypothesis tests to determine the most accurate statistical predictions. Artificial Intelligence Stack Exchange is a question and answer site for people interested in conceptual questions about life and challenges in a world where “cognitive” functions can be mimicked in purely digital environment. Not all characteristics of a process are quantifiable, but the impact on final deliverables can always be measured. It’s always a good idea to look for opportunities to leverage data tools and metrics. You need to make sure you stay in touch with the things that your customers are really concerned about with your products or services.
What’s the difference between robustness and reliability?
Traders that use computerized trading systems to analyze and trade markets using technical analysis do so by developing, testing, and optimizing statistical models based on the application of technical indicators to the price data of a security. This is typically done by looking at historical or past price data, along with market indicators, and identifying situations that have high probabilities of success in the future. Programs and software are tools focused on a very specific task, and thus aren’t generalized and flexible.[4] However, observations in systems such as the internet or biological systems demonstrate adaptation to their environments. One of the ways biological systems adapt to environments is through the use of redundancy.[4] Many organs are redundant in humans. Humans generally only need one kidney, but having a second kidney allows room for failure. This same principle may be taken to apply to software, but there are some challenges.
Generalizing test cases is an example of just one technique to deal with failure—specifically, failure due to invalid user input. Systems generally may also fail due to other reasons as well, such as disconnecting from a network. It can be used to describe an organization that’s grown to a significant size, a person with a lot of natural stamina or the hearty flavor of a gourmet soup. However, in the context of process management, robustness describes the ability of a process to handle unexpected or sub-standard input without compromising profitability or product quality. A trading model is considered robust if it is consistently profitable regardless of market direction.
I saw some papers discuss different things (e.g. attacked model, fault model, noisy data, etc.) when they talk about these terms. Robust doesn’t always mean big, but it always helps keep your company growing in that direction. The recent pandemic was only one of many examples of the type of crisis or shift that can demand robustness across entire industries. Whether you are reducing variability on a micro or macro scale, it’s always better to think robust. The operator of a small food truck serves items like hotdogs, hamburgers and side items to customers at a popular location.
But as a system adds more logic, components, and increases in size, it becomes more complex. Thus, when making a more redundant system, the system also becomes more complex and developers must consider balancing redundancy with complexity. Even though the term is nebulous in general, the robustness of a process is usually quantifiable through analysis of operational performance and output cost or quality. A robust process is one that can handle variations in different types of input successfully. Process designers also need to identify critical process parameters (CPPs) for each key process based on the critical quality attributes. Processes that directly or greatly impact key attributes are the ones that need to be robust.
The best place to incorporate robustness is during the initial research and development phase. That’s why it’s important for businesses to understand their critical parameters and attributes as quickly as possible. After assessing the situation, the operator decides to improve the robustness of the process by investing in a larger cooking surface. This allows him to cook burgers at a lower temperature since he can do more simultaneously. This reduces the risk of burning or under-cooking the food if his attention is on customer service or another food item.
Incrementally increasing variability of each type of input to gauge impact on output is the simplest way to gauge its overall tolerance to change. Robust programming is a style of programming that focuses on handling unexpected termination and unexpected actions.[7] It requires code to handle these terminations and actions gracefully by displaying accurate and unambiguous error messages. Another commonly unforeseen circumstance is when war erupts between major countries. Many financial variables can be impacted due to war, which causes models that are not robust to function erratically.
These examples are programmatically compiled from various online sources to illustrate current usage of the word ‘robust.’ Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Robustness isn’t a complicated subject and it’s one that often comes naturally from following basic best practices in research, development and implementation.
Given that these conditions of a study are met, the models can be verified to be true through the use of mathematical proofs. Robust statistics, therefore, are any statistics that yield good performance when data is drawn from a wide range of probability distributions that are largely unaffected by outliers or small departures from model assumptions in a given dataset. Before worrying about robustness, companies need to know the critical quality attributes (CQAs) of each product or service they deliver to customers. These attributes are the ones that are most essential to the value of the solution to the final recipient.
This depends entirely on the type of process as well as the critical parameters and quality attributes. Typically, processes are made more robust by investing in different equipment, changing technique or shifting priorities. Ultimately, a robust process will simply outperform one that isn’t, especially when circumstances aren’t ideal. It’s up to the process designers to balance the investment cost of robustness versus the potential value addition. 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. In statistics, the term robust or robustness refers to the strength of a statistical model, tests, and procedures according to the specific conditions of the statistical analysis a study hopes to achieve.