Search results
Results from the WOW.Com Content Network
Historically, backtesting was only performed by large institutions and professional money managers due to the expense of obtaining and using detailed datasets. However, backtesting is increasingly used on a wider basis, and independent web-based backtesting platforms have emerged. Although the technique is widely used, it is prone to weaknesses ...
In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and general performance.
Before doing the back-testing or optimization, one needs to set up the data required which is the historical data of a specific time period. This historical data segment is divided into the following two types: In-Sample Data: It is a past segment of market data (historical data) reserved for testing purposes. This data is used for the initial ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
For large samples such as the example below, the binomial distribution is well approximated by convenient continuous distributions, and these are used as the basis for alternative tests that are much quicker to compute, such as Pearson's chi-squared test and the G-test. However, for small samples these approximations break down, and there is no ...
An integrated outline is a helpful step in the process of organizing and writing a scholarly paper (literature review, research paper, thesis or dissertation). When completed the integrated outline contains the relevant scholarly sources (author's last name, publication year, page number if quote) for each section in the outline.
Differential testing is also called back-to-back testing. Differential testing finds semantic bugs by using different implementations of the same functionality as cross-referencing oracles, pinpointing differences in their outputs over the same input: any discrepancy between the program behaviors on the same input is marked as a potential bug.
This simple example for the case of mean estimation is just to illustrate the construction of a jackknife estimator, while the real subtleties (and the usefulness) emerge for the case of estimating other parameters, such as higher moments than the mean or other functionals of the distribution.